Paper Digest: Recent Papers on Question Answering
Paper Digest Team extracted all recent Question Answering related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic. Based in New York, Paper Digest is dedicated to producing high-quality text analysis results that people can acturally use on a daily basis. In the past 4 years, we have been serving users across the world with a number of exclusive services on ranking, search, tracking and review. This month we feature Literature Review Generator, which automatically generates literature review around any topic.
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TABLE 1: Paper Digest: Recent Papers on Question Answering
| Paper | Author(s) | Source | Date | |
|---|---|---|---|---|
| 1 | Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Visual question answering (VQA) is the multi-modal task of answering natural language questions about an input image. Through cross-dataset adaptation methods, it is possible to … |
Arjun R. Akula; | arxiv-cs.CV | 2022-01-24 |
| 2 | Towards Collaborative Question Answering: A Preliminary Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose CollabQA, a novel QA task in which several expert agents coordinated by a moderator work together to answer questions that cannot be answered with any single agent alone. |
XIANGKUN HU et. al. | arxiv-cs.AI | 2022-01-24 |
| 3 | Artefact Retrieval: Overview of NLP Models with Knowledge Base Access Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we systematically describe the typology of artefacts (items retrieved from a knowledge base), retrieval mechanisms and the way these artefacts are fused into the model. |
Vilém Zouhar; Marius Mosbach; Debanjali Biswas; Dietrich Klakow; | arxiv-cs.CL | 2022-01-24 |
| 4 | Unified Question Generation with Continual Lifelong Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats. |
WEI YUAN et. al. | arxiv-cs.CL | 2022-01-24 |
| 5 | Question Rewriting? Assessing Its Importance for Conversational Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a conversational question answering system designed specifically for the Search-Oriented Conversational AI (SCAI) shared task, and reports on a detailed analysis of its question rewriting module. |
Gonçalo Raposo; Rui Ribeiro; Bruno Martins; Luísa Coheur; | arxiv-cs.CL | 2022-01-22 |
| 6 | Leaf: Multiple-Choice Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we present Leaf, a system for generating multiple-choice questions from factual text. |
KRISTIYAN VACHEV et. al. | arxiv-cs.CL | 2022-01-22 |
| 7 | Conversational Information Seeking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. |
Hamed Zamani; Johanne R. Trippas; Jeff Dalton; Filip Radlinski; | arxiv-cs.IR | 2022-01-21 |
| 8 | GreaseLM: Graph REASoning Enhanced Language Models for Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose GreaseLM, a new model that fuses encoded representations from pretrained LMs and graph neural networks over multiple layers of modality interaction operations. |
XIKUN ZHANG et. al. | arxiv-cs.CL | 2022-01-21 |
| 9 | Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent A Replication Crisis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we survey and analyze a wide range of evaluation results with significant coverage of 100 publications and 98 systems from the last decade. |
ALEKSANDR PEREVALOV et. al. | arxiv-cs.CL | 2022-01-20 |
| 10 | Evaluating Machine Common Sense Via Cloze Testing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the use of cloze testing combined with word embeddings to measure the LM’s robustness and confidence. |
Ehsan Qasemi; Lee Kezar; Jay Pujara; Pedro Szekely; | arxiv-cs.CL | 2022-01-19 |
| 11 | Improving Biomedical Information Retrieval with Neural Retrievers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical domain, and achieve this goal using a three-pronged approach. |
Man Luo; Arindam Mitra; Tejas Gokhale; Chitta Baral; | arxiv-cs.IR | 2022-01-19 |
| 12 | Expert Finding in Legal Community Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose methods for generating query-dependent textual profiles for lawyers covering several aspects including sentiment, comments, and recency. |
Arian Askari; Suzan Verberne; Gabriella Pasi; | arxiv-cs.IR | 2022-01-19 |
| 13 | Korean-Specific Dataset for Table Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate how we construct Korean-specific datasets for table question answering: Korean tabular dataset is a collection of 1.4M tables with corresponding descriptions for unsupervised pre-training language models. |
CHANGWOOK JUN et. al. | arxiv-cs.CL | 2022-01-17 |
| 14 | Generalizable Neuro-symbolic Systems for Commonsense Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. |
Alessandro Oltramari; Jonathan Francis; Filip Ilievski; Kaixin Ma; Roshanak Mirzaee; | arxiv-cs.CL | 2022-01-17 |
| 15 | Natural Language Deduction Through Search Over Statement Compositions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a system for natural language deduction that decomposes the task into separate steps coordinated by best-first search, producing a tree of intermediate conclusions that faithfully reflects the system’s reasoning process. |
Kaj Bostrom; Zayne Sprague; Swarat Chaudhuri; Greg Durrett; | arxiv-cs.CL | 2022-01-16 |
| 16 | In Situ Answer Sentence Selection at Web-scale Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Passage-based Extracting Answer Sentence In-place (PEASI), a novel design for AS2 optimized for Web-scale setting, that, instead, computes such answer without processing each candidate individually. |
Zeyu Zhang; Thuy Vu; Alessandro Moschitti; | arxiv-cs.CL | 2022-01-16 |
| 17 | Double Retrieval and Ranking for Accurate Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address both drawbacks by proposing (i) a double reranking model, which, for each target answer, selects the best support; and (ii) a second neural retrieval stage designed to encode question and answer pair as the query, which finds more specific verification information. |
Zeyu Zhang; Thuy Vu; Alessandro Moschitti; | arxiv-cs.CL | 2022-01-16 |
| 18 | A Benchmark for Generalizable and Interpretable Temporal Question Answering Over Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a benchmark dataset for temporal reasoning, TempQA-WD, to encourage research in extending the present approaches to target a more challenging set of complex reasoning tasks. |
SUMIT NEELAM et. al. | arxiv-cs.CL | 2022-01-15 |
| 19 | Ensemble Transformer for Efficient and Accurate Ranking Tasks: An Application to Question Answering Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the following research question: How can we make the AS2models more accurate without significantly increasing their model complexity? |
Yoshitomo Matsubara; Luca Soldaini; Eric Lind; Alessandro Moschitti; | arxiv-cs.CL | 2022-01-15 |
| 20 | Reasoning Over Hybrid Chain for Table-and-Text Open Domain QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a ChAin-centric Reasoning and Pre-training framework (CARP). |
WANJUN ZHONG et. al. | arxiv-cs.CL | 2022-01-15 |
| 21 | Kformer: Knowledge Injection in Transformer Feed-Forward Layers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore the FFN in Transformer and propose a novel knowledge fusion model, namely Kformer, which incorporates external knowledge through the feed-forward layer in Transformer. |
YUNZHI YAO et. al. | arxiv-cs.CL | 2022-01-14 |
| 22 | Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. |
Yuyan Chen; Yanghua Xiao; Bang Liu; | arxiv-cs.CL | 2022-01-13 |
| 23 | A Thousand Words Are Worth More Than A Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we call for a paradigm shift for the OK-VQA task, which transforms the image into plain text, so that we can enable knowledge passage retrieval, and generative question-answering in the natural language space. |
FENG GAO et. al. | arxiv-cs.CV | 2022-01-13 |
| 24 | Towards Automated Error Analysis: Learning to Characterize Errors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel form of meta learning that automatically learns interpretable rules that characterize the types of errors that a system makes, and demonstrate these rules’ ability to help understand and improve two NLP systems. |
Tong Gao; Shivang Singh; Raymond J. Mooney; | arxiv-cs.CL | 2022-01-13 |
| 25 | On The Efficacy of Co-Attention Transformer Layers in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the efficacy of co-attention transformer layers in helping the network focus on relevant regions while answering the question. |
Ankur Sikarwar; Gabriel Kreiman; | arxiv-cs.CV | 2022-01-11 |
| 26 | SCROLLS: Standardized CompaRison Over Long Language Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce SCROLLS, a suite of tasks that require reasoning over long texts. |
URI SHAHAM et. al. | arxiv-cs.CL | 2022-01-10 |
| 27 | RxWhyQA: A Clinical Question-answering Dataset with The Challenge of Multi-answer Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Materials and Methods We leveraged the annotated relations from the 2018 National NLP Clinical Challenges (n2c2) corpus to generate a QA dataset. |
Sungrim Moon; Huan He; Hongfang Liu; Jungwei W. Fan; | arxiv-cs.CL | 2022-01-07 |
| 28 | Does Entity Abstraction Help Generative Transformers Reason? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose and empirically explore three ways to add such abstraction: (i) as additional input embeddings, (ii) as a separate sequence to encode, and (iii) as an auxiliary prediction task for the model. |
Nicolas Gontier; Siva Reddy; Christopher Pal; | arxiv-cs.CL | 2022-01-05 |
| 29 | Multi Document Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a study on Reading Comprehension and its evolution in Natural Language Processing over the past few decades. |
Avi Chawla; | arxiv-cs.CL | 2022-01-05 |
| 30 | Interactive Attention AI to Translate Low Light Photos to Captions for Night Scene Understanding in Women Safety Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For the first time in literature, this paper develops a Deep Learning model that translates night scenes to sentences, opening new possibilities for AI applications in the safety of visually impaired women. |
Rajagopal A; Nirmala V; Arun Muthuraj Vedamanickam; | arxiv-cs.CV | 2022-01-03 |
| 31 | An Earthworks Quality Assurance Methodology Which Avoids Unreliable Correlations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: In earthworks, testing using density ratio is applied widely in quality control. Density ratio tests take significant time for results to be reported. Yet because of its … |
Burt G. Look; | 2022-01-01 | |
| 32 | Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we instead focus on better utilizing the \textit{implicit knowledge} stored in pre-trained language models. |
Zi-Yi Dou; Nanyun Peng; | arxiv-cs.CL | 2022-01-01 |
| 33 | Visual Question Answering By Pattern Matching and Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details |
Huayi Zhan; Peixi Xiong; Xin Wang; Xin WANG; Lan Yang; | Neurocomputing | 2022-01-01 |
| 34 | OpenQA: Hybrid QA System Relying on Structured Knowledge Base As Well As Non-structured Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an intelligent question-answering system based on structured KB and unstructured data, called OpenQA, in which users can give query questions and the model can quickly give accurate answers back to users. |
GAOCHEN WU et. al. | arxiv-cs.CL | 2021-12-31 |
| 35 | What Is Event Knowledge Graph: A Survey Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper provides a comprehensive survey of EKG from history, ontology, instance, and application views. |
SAIPING GUAN et. al. | arxiv-cs.LG | 2021-12-30 |
| 36 | VisQA: Quantifying Information Visualisation Recallability Via Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we propose a visual question answering (VQA) paradigm to study visualisation recallability and present VisQA — a novel VQA dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N~=~305) recallability scores obtained from 1,000 questions from five question types. |
Yao Wang; Chuhan Jiao; Mihai Bâce; Andreas Bulling; | arxiv-cs.HC | 2021-12-30 |
| 37 | Does QA-based Intermediate Training Help Fine-tuning Language Models for Text Classification? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, using the SQuAD-2.0 QA task for intermediate training for target text classification tasks, we experimented on eight tasks for single-sequence classification and eight tasks for sequence-pair classification using two base and two compact language models. |
Shiwei Zhang; Xiuzhen Zhang; | arxiv-cs.CL | 2021-12-30 |
| 38 | HeteroQA: Learning Towards Question-and-Answering Through Multiple Information Sources Via Heterogeneous Graph Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose a question-aware heterogeneous graph transformer to incorporate the MIS in the user community to automatically generate the answer. |
SHEN GAO et. al. | arxiv-cs.CL | 2021-12-27 |
| 39 | Multi-Image Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an empirical study of different feature extraction methods with different loss functions. |
Harsh Raj; Janhavi Dadhania; Akhilesh Bhardwaj; | arxiv-cs.CV | 2021-12-27 |
| 40 | A Survey on Non-English Question Answering Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we review question answering datasets that are available in common languages other than English such as French, German, Japanese, Chinese, Arabic, Russian, as well as the multilingual and cross-lingual question-answering datasets. |
Andreas Chandra; Affandy Fahrizain; Ibrahim; Simon Willyanto Laufried; | arxiv-cs.CL | 2021-12-27 |
| 41 | A Passage to India: Pre-trained Word Embeddings for Indian Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we use various existing approaches to create multiple word embeddings for 14 Indian languages. |
Kumar Saurav; Kumar Saunack; Diptesh Kanojia; Pushpak Bhattacharyya; | arxiv-cs.CL | 2021-12-27 |
| 42 | The University of Texas at Dallas HLTRI’s Participation in EPIC-QA: Searching for Entailed Questions Revealing Novel Answer Nuggets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes our participation in both tasks of EPIC-QA, targeting: (1) Expert QA and (2) Consumer QA. |
Maxwell Weinzierl; Sanda M. Harabagiu; | arxiv-cs.CL | 2021-12-27 |
| 43 | Does CLIP Benefit Visual Question Answering in The Medical Domain As Much As It Does in The General Domain? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we present PubMedCLIP, a fine-tuned version of CLIP for the medical domain based on PubMed articles. |
Sedigheh Eslami; Gerard de Melo; Christoph Meinel; | arxiv-cs.CV | 2021-12-27 |
| 44 | Towards Personalized Answer Generation in E-Commerce Via Multi-Perspective Preference Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this challenge, we propose a novel Personalized Answer GEneration method (PAGE) with multi-perspective preference modeling, which explores historical user-generated contents to model user preference for generating personalized answers in PQA. |
Yang Deng; Yaliang Li; Wenxuan Zhang; Bolin Ding; Wai Lam; | arxiv-cs.CL | 2021-12-27 |
| 45 | New Methods & Metrics for LFQA Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work addresses every one these critical bottlenecks, contributing natural language inference/generation (NLI/NLG) methods and metrics that make significant strides to their alleviation. |
Suchismit Mahapatra; Vladimir Blagojevic; Pablo Bertorello; Prasanna Kumar; | arxiv-cs.CL | 2021-12-26 |
| 46 | ArT: All-round Thinker for Unsupervised Commonsense Question-Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by human thinking experience, we propose an approach of All-round Thinker (ArT) by fully taking association during knowledge generating. |
Jiawei Wang; Hai Zhao; | arxiv-cs.CL | 2021-12-26 |
| 47 | PerCQA: Persian Community Question Answering Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present PerCQA, the first Persian dataset for CQA. |
Naghme Jamali; Yadollah Yaghoobzadeh; Hesham Faili; | arxiv-cs.CL | 2021-12-25 |
| 48 | CLEVR3D: Compositional Language and Elementary Visual Reasoning for Question Answering in 3D Real-World Scenes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the Visual Question Answering task in 3D real-world scenes (VQA-3D), which aims to answer all possible questions given a 3D scene. |
XU YAN et. al. | arxiv-cs.CV | 2021-12-22 |
| 49 | An Inference Approach To Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs. |
Aayushee Gupta; K. M. Annervaz; Ambedkar Dukkipati; Shubhashis Sengupta; | arxiv-cs.LG | 2021-12-21 |
| 50 | An ASP-based Approach to Answering Natural Language Questions for Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. |
Dhruva Pendharkar; Kinjal Basu; Farhad Shakerin; Gopal Gupta; | arxiv-cs.CL | 2021-12-21 |
| 51 | ScanQA: 3D Question Answering for Spatial Scene Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a baseline model for 3D-QA, named ScanQA model, where the model learns a fused descriptor from 3D object proposals and encoded sentence embeddings. |
Daichi Azuma; Taiki Miyanishi; Shuhei Kurita; Motoki Kawanabe; | arxiv-cs.CV | 2021-12-20 |
| 52 | MuMuQA: Multimedia Multi-Hop News Question Answering Via Cross-Media Knowledge Extraction and Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new QA evaluation benchmark with 1,384 questions over news articles that require cross-media grounding of objects in images onto text. |
REVANTH GANGI REDDY et. al. | arxiv-cs.CL | 2021-12-20 |
| 53 | General Greedy De-bias Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. |
Xinzhe Han; Shuhui Wang; Chi Su; Qingming Huang; Qi Tian; | arxiv-cs.LG | 2021-12-20 |
| 54 | Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. |
Hariom A. Pandya; Bhavik Ardeshna; Dr. Brijesh S. Bhatt; | arxiv-cs.CL | 2021-12-18 |
| 55 | WebGPT: Browser-assisted Question-answering with Human Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We train and evaluate our models on ELI5, a dataset of questions asked by Reddit users. |
REIICHIRO NAKANO et. al. | arxiv-cs.CL | 2021-12-17 |
| 56 | ActKnow: Active External Knowledge Infusion Learning for Question Answering in Low Data Regime Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a technique called ActKnow that actively infuses knowledge from Knowledge Graphs (KG) based on-demand into learning for Question Answering (QA). |
K. M. Annervaz; Pritam Kumar Nath; Ambedkar Dukkipati; | arxiv-cs.LG | 2021-12-17 |
| 57 | Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-hop reasoning chain based adversarial attack method. |
Jiayu Ding; Siyuan Wang; Qin Chen; Zhongyu Wei; | arxiv-cs.CL | 2021-12-17 |
| 58 | DREAM: Uncovering Mental Models Behind Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose DREAM, a model that takes a situational question as input to produce a mental model elaborating the situation, without any additional task specific training data for mental models. |
Yuling Gu; Bhavana Dalvi Mishra; Peter Clark; | arxiv-cs.CL | 2021-12-16 |
| 59 | Models in The Loop: Aiding Crowdworkers with Generative Annotation Assistants Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we examine if we can maintain the advantages of DADC, without suffering the additional cost. |
MAX BARTOLO et. al. | arxiv-cs.CL | 2021-12-16 |
| 60 | Distilled Dual-Encoder Model for Vision-Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a cross-modal attention distillation framework to train a dual-encoder model for vision-language understanding tasks, such as visual reasoning and visual question answering. |
ZEKUN WANG et. al. | arxiv-cs.CL | 2021-12-16 |
| 61 | Utilizing Evidence Spans Via Sequence-Level Contrastive Learning for Long-Context Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel method for equipping long-range transformers with an additional sequence-level objective for better identification of supporting evidence spans. |
Avi Caciularu; Ido Dagan; Jacob Goldberger; Arman Cohan; | arxiv-cs.CL | 2021-12-16 |
| 62 | Ditch The Gold Standard: Re-evaluating Conversational Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we conduct the first large-scale human evaluation of state-of-the-art CQA systems, where human evaluators converse with models and judge the correctness of their answers. |
Huihan Li; Tianyu Gao; Manan Goenka; Danqi Chen; | arxiv-cs.CL | 2021-12-16 |
| 63 | Towards Unsupervised Dense Information Retrieval with Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers, and show that it leads to strong retrieval performance. |
GAUTIER IZACARD et. al. | arxiv-cs.IR | 2021-12-16 |
| 64 | LongT5: Efficient Text-To-Text Transformer for Long Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new model, called LongT5, with which we explore the effects of scaling both the input length and model size at the same time. |
MANDY GUO et. al. | arxiv-cs.CL | 2021-12-15 |
| 65 | QuALITY: Question Answering with Long Input Texts, Yes! Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process. |
RICHARD YUANZHE PANG et. al. | arxiv-cs.CL | 2021-12-15 |
| 66 | Nirikshak: An Autonomous Testing Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This project is an effort to make such a framework. |
Yash Mahalwal; Pawel Pratyush; Yogesh Poonia; | arxiv-cs.SE | 2021-12-15 |
| 67 | QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we conduct an extensive comparison of entailment and QA-based metrics, demonstrating that carefully choosing the components of a QA-based metric is critical to performance. |
Alexander R. Fabbri; Chien-Sheng Wu; Wenhao Liu; Caiming Xiong; | arxiv-cs.CL | 2021-12-15 |
| 68 | Learning Rich Representation of Keyphrases from Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore how to learn task-specific language models aimed towards learning rich representation of keyphrases from text documents. |
Mayank Kulkarni; Debanjan Mahata; Ravneet Arora; Rajarshi Bhowmik; | arxiv-cs.CL | 2021-12-15 |
| 69 | 3D Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the first attempt at extending VQA to the 3D domain, which can facilitate artificial intelligence’s perception of 3D real-world scenarios. |
Shuquan Ye; Dongdong Chen; Songfang Han; Jing Liao; | arxiv-cs.CV | 2021-12-15 |
| 70 | CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in context into a standalone question. |
Zeqiu Wu; Yi Luan; Hannah Rashkin; David Reitter; Gaurav Singh Tomar; | arxiv-cs.CL | 2021-12-15 |
| 71 | Block-Skim: Efficient Question Answering for Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Following this motivation, we propose Block-skim, which learns to skim unnecessary context in higher hidden layers to improve and accelerate the Transformer performance. |
YUE GUAN et. al. | arxiv-cs.CL | 2021-12-15 |
| 72 | Learning to Transpile AMR Into SPARQL Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA). |
MIHAELA BORNEA et. al. | arxiv-cs.CL | 2021-12-14 |
| 73 | Multi-Instance Training for Question Answering Across Table and Linked Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Answering natural language questions using information from tables (TableQA) is of considerable recent interest. In many applications, tables occur not in isolation, but embedded … |
VISHWAJEET KUMAR et. al. | arxiv-cs.CL | 2021-12-14 |
| 74 | Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on these two problems and propose a Graph Matching Attention (GMA) network. |
JianJian Cao; Xiameng Qin; Sanyuan Zhao; Jianbing Shen; | arxiv-cs.CV | 2021-12-14 |
| 75 | Dual-Key Multimodal Backdoors for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that multimodal networks are vulnerable to a novel type of attack that we refer to as Dual-Key Multimodal Backdoors. |
Matthew Walmer; Karan Sikka; Indranil Sur; Abhinav Shrivastava; Susmit Jha; | arxiv-cs.CV | 2021-12-14 |
| 76 | Do Answers to Boolean Questions Need Explanations? Yes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We confirm our findings with a user study which shows that our extracted evidence spans enhance the user experience. |
Sara Rosenthal; Mihaela Bornea; Avirup Sil; Radu Florian; Scott McCarley; | arxiv-cs.CL | 2021-12-14 |
| 77 | Learning to Retrieve Passages Without Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We observe that existing pretrained models for retrieval struggle in this scenario, and propose a new pretraining scheme designed for retrieval: recurring span retrieval. |
Ori Ram; Gal Shachaf; Omer Levy; Jonathan Berant; Amir Globerson; | arxiv-cs.CL | 2021-12-14 |
| 78 | Scaling Up Query-Focused Summarization to Meet Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we show how to extend this task to make it more realistic. |
Weijia Zhang; Svitlana Vakulenko; Thilina Rajapakse; Evangelos Kanoulas; | arxiv-cs.CL | 2021-12-14 |
| 79 | You Only Need One Model for Open-domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose casting the retriever and the reranker as hard-attention mechanisms applied sequentially within the transformer architecture and feeding the resulting computed representations to the reader. |
HAEJUN LEE et. al. | arxiv-cs.CL | 2021-12-14 |
| 80 | Improving and Diagnosing Knowledge-Based Visual Question Answering Via Entity Enhanced Knowledge Injection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we empirically study how and whether such methods, applied in a bi-modal setting, can improve an existing VQA system’s performance on the KBVQA task. |
Diego Garcia-Olano; Yasumasa Onoe; Joydeep Ghosh; | arxiv-cs.CL | 2021-12-13 |
| 81 | Explanation Container in Case-Based Biomedical Question-Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the design of the Explanatory Agent (xARA), a case-based ARA that answers biomedical queries by accessing multiple KPs, ranking results, and explaining the ranking of results. |
Prateek Goel; Adam J. Johs; Manil Shrestha; Rosina O. Weber; | arxiv-cs.AI | 2021-12-13 |
| 82 | Roof-BERT: Divide Understanding Labour and Join in Work Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the problem, we propose Roof-BERT, a model with two underlying BERTs and a fusion layer on them. |
Wei-Lin Liao; Wei-Yun Ma; | arxiv-cs.CL | 2021-12-13 |
| 83 | Video As Conditional Graph Hierarchy for Multi-Granular Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To align with the multi-granular essence of linguistic concepts in language queries, we propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner, with the guidance of corresponding textual cues. |
JUNBIN XIAO et. al. | arxiv-cs.CV | 2021-12-12 |
| 84 | Change Detection Meets Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multi-temporal aerial images. |
Zhenghang Yuan; Lichao Mou; Zhitong Xiong; Xiaoxiang Zhu; | arxiv-cs.CV | 2021-12-12 |
| 85 | Injecting Numerical Reasoning Skills Into Knowledge Base Question Answering Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new embedding-based KBQA framework which particularly takes numerical reasoning into account. |
YU FENG et. al. | arxiv-cs.CL | 2021-12-11 |
| 86 | TempoQR: Temporal Question Reasoning Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. |
COSTAS MAVROMATIS et. al. | arxiv-cs.CL | 2021-12-10 |
| 87 | Improving The Question Answering Quality Using Answer Candidate Filtering Based on Natural-Language Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main contribution is an approach capable of identifying wrong answers provided by a QA system. |
Aleksandr Gashkov; Aleksandr Perevalov; Maria Eltsova; Andreas Both; | arxiv-cs.IR | 2021-12-10 |
| 88 | Improving Language Models By Retrieving from Trillions of Tokens Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. |
SEBASTIAN BORGEAUD et. al. | arxiv-cs.CL | 2021-12-08 |
| 89 | Natural Answer Generation: From Factoid Answer to Full-length Answer Using Grammar Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a system that outputs a full-length answer given a question and the extracted factoid answer (short spans such as named entities) as the input. |
Manas Jain; Sriparna Saha; Pushpak Bhattacharyya; Gladvin Chinnadurai; Manish Kumar Vatsa; | arxiv-cs.CL | 2021-12-07 |
| 90 | Qualitative Analysis for Human Centered AI Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we argue that qualitative analysis (QA) can be a valuable tool in this process, supplementing, informing, and extending the possibilities of AI models. |
Orestis Papakyriakopoulos; Elizabeth Anne Watkins; Amy Winecoff; Klaudia Jaźwińska; Tithi Chattopadhyay; | arxiv-cs.HC | 2021-12-07 |
| 91 | Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this review work, the research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. |
Hariom A. Pandya; Brijesh S. Bhatt; | arxiv-cs.CL | 2021-12-07 |
| 92 | Semantic Answer Type and Relation Prediction Task (SMART 2021) Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Each year the International Semantic Web Conference organizes a set of Semantic Web Challenges to establish competitions that will advance state-of-the-art solutions in some problem domains. |
NANDANA MIHINDUKULASOORIYA et. al. | arxiv-cs.CL | 2021-12-07 |
| 93 | Automated Story Generation As Question-Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel approach to automated story generation that treats the problem as one of generative question-answering. |
Louis Castricato; Spencer Frazier; Jonathan Balloch; Nitya Tarakad; Mark Riedl; | arxiv-cs.CL | 2021-12-07 |
| 94 | MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the above issues, we propose a novel model named MoCA, which incorporates multi-stage domain pretraining and multimodal cross attention for the TQA task. |
FANGZHI XU et. al. | arxiv-cs.MM | 2021-12-06 |
| 95 | JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel model, JointLK, which solves the above limitations through the joint reasoning of LMs and GNNs and the dynamic KGs pruning mechanism. |
Yueqing Sun; Qi Shi; Le Qi; Yu Zhang; | arxiv-cs.CL | 2021-12-05 |
| 96 | A Russian Jeopardy! Data Set for Question-Answering Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Chgk (che ge ka). |
Elena Mikhalkova; | arxiv-cs.CL | 2021-12-04 |
| 97 | Multitask Finetuning for Improving Neural Machine Translation in Indian Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a Multitask Finetuning methodology which combines the Bilingual Machine Translation task with an auxiliary Causal Language Modeling task to improve performance on the former task on Indian Languages. |
Shaily Desai; Atharva Kshirsagar; Manisha Marathe; | arxiv-cs.CL | 2021-12-03 |
| 98 | MetaQA: Combining Expert Agents for Multi-Skill Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer candidates. |
Haritz Puerto; Gözde Gül Şahin; Iryna Gurevych; | arxiv-cs.CL | 2021-12-03 |
| 99 | Action Based Network for Conversation Question Reformulation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an action-based approach to recover the complete expression of the question. |
Zheyu Ye; Jiangning Liu; Qian Yu; Jianxun Ju; | arxiv-cs.CL | 2021-11-29 |
| 100 | Classification-Regression for Chart Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Charts are a popular and effective form of data visualization. Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different … |
Matan Levy; Rami Ben-Ari; Dani Lischinski; | arxiv-cs.CV | 2021-11-29 |
| 101 | LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a Lightweight Visual-Linguistic Reasoning framework named LiVLR. |
Jingjing Jiang; Ziyi Liu; Nanning Zheng; | arxiv-cs.CV | 2021-11-29 |
| 102 | AssistSR: Affordance-centric Question-driven Video Segment Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast, we present a new task called Affordance-centric Question-driven Video Segment Retrieval (AQVSR). |
Stan Weixian Lei; Yuxuan Wang; Dongxing Mao; Difei Gao; Mike Zheng Shou; | arxiv-cs.CV | 2021-11-29 |
| 103 | A Grounded Well-being Conversational Agent with Multiple Interaction Modes: Preliminary Results Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we seek to address this limitation via a conversational agent that adopts one aspect of in-person doctor-patient interactions: A human avatar to facilitate medical grounded question answering. |
Xinxin Yan; Ndapa Nakashole; | arxiv-cs.CL | 2021-11-28 |
| 104 | Tapping BERT for Preposition Sense Disambiguation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel methodology for preposition sense disambiguation (PSD), which does not use any linguistic tools. |
Siddhesh Pawar; Shyam Thombre; Anirudh Mittal; Girishkumar Ponkiya; Pushpak Bhattacharyya; | arxiv-cs.CL | 2021-11-27 |
| 105 | Answer Generation for Questions With Multiple Information Sources in E-Commerce Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Through this work we propose a novel pipeline (MSQAP) that utilizes the rich information present in the aforementioned sources by separately performing relevancy and ambiguity prediction before generating a response. |
Anand A. Rajasekar; Nikesh Garera; | arxiv-cs.CL | 2021-11-27 |
| 106 | Scene Graph Generation with Geometric Context Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduced a post-processing algorithm called Geometric Context to understand the visual scenes better geometrically. |
Vishal Kumar; Albert Mundu; Satish Kumar Singh; | arxiv-cs.CV | 2021-11-25 |
| 107 | Zero-Shot Open-Book Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). |
Sia Gholami; Mehdi Noori; | arxiv-cs.CL | 2021-11-22 |
| 108 | Many Heads But One Brain: An Overview of Fusion Brain Challenge on AI Journey 2021 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Supporting the current trend in the AI community, we propose the AI Journey 2021 Challenge called Fusion Brain which is targeted to make the universal architecture process different modalities (namely, images, texts, and code) and to solve multiple tasks for vision and language. |
DARIA BAKSHANDAEVA et. al. | arxiv-cs.CV | 2021-11-21 |
| 109 | Human-Adversarial Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to stress test VQA models, we benchmark them against human-adversarial examples. |
SASHA SHENG et. al. | nips | 2021-11-20 |
| 110 | Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Scallop, a system that builds upon probabilistic deductive databases, to bridge this gap. |
JIANI HUANG et. al. | nips | 2021-11-20 |
| 111 | SQALER: Scaling Question Answering By Decoupling Multi-Hop and Logical Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this issue by showing that multi-hop and more complex logical reasoning can be accomplished separately without losing expressive power. |
Mattia Atzeni; Jasmina Bogojeska; Andreas Loukas; | nips | 2021-11-20 |
| 112 | Vector-valued Distance and Gyrocalculus on The Space of Symmetric Positive Definite Matrices Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the use of the vector-valued distance to compute distances and extract geometric information from the manifold of symmetric positive definite matrices (SPD), and develop gyrovector calculus, constructing analogs of vector space operations in this curved space. |
Federico Lopez; Beatrice Pozzetti; Steve Trettel; Michael Strube; Anna Wienhard; | nips | 2021-11-20 |
| 113 | One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Cross-lingual Open-Retrieval Answer Generation (CORA), the first unified many-to-many question answering (QA) model that can answer questions across many languages, even for ones without language-specific annotated data or knowledge sources. |
Akari Asai; Xinyan Yu; Jungo Kasai; Hanna Hajishirzi; | nips | 2021-11-20 |
| 114 | Introspective Distillation for Robust Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel debiasing method called Introspective Distillation (IntroD) to make the best of both worlds for QA. |
Yulei Niu; Hanwang Zhang; | nips | 2021-11-20 |
| 115 | Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our proposed model combines a novel subgraph matching networks to capture global interactions in both question and subgraphs and an Enhanced Bilateral Multi-Perspective Matching model to capture local interactions. |
HANNING GAO et. al. | arxiv-cs.CL | 2021-11-20 |
| 116 | Debiased Visual Question Answering from Feature and Sample Perspectives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method named D-VQA to alleviate the above challenges from the feature and sample perspectives. |
Zhiquan Wen; Guanghui Xu; Mingkui Tan; Qingyao Wu; Qi Wu; | nips | 2021-11-20 |
| 117 | Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To consider the interdependent knowledge between contextual clips into the network inference, we propose a Siamese Sampling and Reasoning (SiaSamRea) approach, which consists of a siamese sampling mechanism to generate sparse and similar clips (i.e., siamese clips) from the same video, and a novel reasoning strategy for integrating the interdependent knowledge between contextual clips into the network. |
WEIJIANG YU et. al. | nips | 2021-11-20 |
| 118 | End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an end-to-end differentiable training method for retrieval-augmented open-domain question answering systems that combine information from multiple retrieved documents when generating answers. |
Devendra Singh; Siva Reddy; Will Hamilton; Chris Dyer; Dani Yogatama; | nips | 2021-11-20 |
| 119 | Textbook to Triples: Creating Knowledge Graph in The Form of Triples from AI TextBook Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, an effort has been made towards developing a system that could convert the text from a given textbook into triples that can be used to visualize as a knowledge graph and use for further applications. |
Aman Kumar; Swathi Dinakaran; | arxiv-cs.CL | 2021-11-20 |
| 120 | How Modular Should Neural Module Networks Be for Systematic Generalization? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate that the degree of modularity of the NMN have large influence on systematic generalization. |
Vanessa D'Amario; Tomotake Sasaki; Xavier Boix; | nips | 2021-11-20 |
| 121 | Medical Visual Question Answering: A Survey Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to provide comprehensive information for researchers interested in medical artificial intelligence. |
ZHIHONG LIN et. al. | arxiv-cs.CV | 2021-11-19 |
| 122 | UFO: A UniFied TransfOrmer for Vision-Language Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of processing either unimodal inputs (e.g., image or language) or multimodal inputs (e.g., the concatenation of the image and the question), for vision-language (VL) representation learning. |
JIANFENG WANG et. al. | arxiv-cs.CV | 2021-11-18 |
| 123 | Building A Question Answering System for The Manufacturing Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the problem of insufficient training data for the technology question answering system, we propose a method to generate questions according to a declarative sentence from several different dimensions so that multiple question-answer pairs can be obtained from a declarative sentence. |
LIU XINGGUANG et. al. | arxiv-cs.CL | 2021-11-18 |
| 124 | Triple Attention Network Architecture for MovieQA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel network with triple-attention architecture for the inclusion of audio in the Movie QA task. |
Ankit Shah; Tzu-Hsiang Lin; Shijie Wu; | arxiv-cs.MM | 2021-11-18 |
| 125 | Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we define an interpretable reading comprehension (IRC) model as a pipeline model with the capability of predicting unanswerable queries. |
Kosuke Nishida; Kyosuke Nishida; Itsumi Saito; Sen Yoshida; | arxiv-cs.CL | 2021-11-17 |
| 126 | Document AI: Benchmarks, Models and Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. |
Lei Cui; Yiheng Xu; Tengchao Lv; Furu Wei; | arxiv-cs.CL | 2021-11-16 |
| 127 | QA4PRF: A Question Answering Based Framework for Pseudo Relevance Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a QA-based framework for PRF called QA4PRF to utilize contextual information in documents. |
HANDONG MA et. al. | arxiv-cs.IR | 2021-11-16 |
| 128 | Language Bias in Visual Question Answering: A Survey and Taxonomy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct a comprehensive review and analysis of this field for the first time, and classify the existing methods according to three categories, including enhancing visual information, weakening language priors, data enhancement and training strategies. |
Desen Yuan; | arxiv-cs.CV | 2021-11-16 |
| 129 | Achieving Human Parity on Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes our recent research of AliceMind-MMU (ALIbaba’s Collection of Encoder-decoders from Machine IntelligeNce lab of Damo academy – MultiMedia Understanding) that obtains similar or even slightly better results than human being does on VQA. |
MING YAN et. al. | arxiv-cs.CL | 2021-11-16 |
| 130 | Calculating Question Similarity Is Enough: A New Method for KBQA Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, this paper proposes a Corpus Generation – Retrieve Method (CGRM) with Pre-training Language Model (PLM) for the KBQA task. |
Hanyu Zhao; Sha Yuan; Jiahong Leng; Xiang Pan; Guoqiang Wang; | arxiv-cs.CL | 2021-11-15 |
| 131 | Question-Based Salient Span Selection for More Controllable Text Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a method for incorporating question-answering (QA) signals into a summarization model. |
Daniel Deutsch; Dan Roth; | arxiv-cs.CL | 2021-11-15 |
| 132 | Graph Relation Transformer: Incorporating Pairwise Object Features Into The Transformer Architecture Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to overcome these shortcomings, we propose a Graph Relation Transformer (GRT), which uses edge information in addition to node information for graph attention computation in the Transformer. |
Michael Yang; Aditya Anantharaman; Zachary Kitowski; Derik Clive Robert; | arxiv-cs.CV | 2021-11-11 |
| 133 | A Chinese Multi-type Complex Questions Answering Dataset Over Wikidata Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Together with the dataset, we present a text-to-SPARQL baseline model, which can effectively answer multi-type complex questions, such as factual questions, dual intent questions, boolean questions, and counting questions, with Wikidata as the background knowledge. |
JIANYUN ZOU et. al. | arxiv-cs.CL | 2021-11-11 |
| 134 | AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We analyze and benchmark state-of-the-art models on these subtasks and introduce a novel unsupervised approach for multi-perspective data augmentation, that further boosts overall summarization performance according to automatic evaluation. |
Alexander R. Fabbri; Xiaojian Wu; Srini Iyer; Haoran Li; Mona Diab; | arxiv-cs.CL | 2021-11-11 |
| 135 | Automated Question Generation and Question Answering from Turkish Texts Using Text-to-text Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we fine-tune a multilingual T5 (mT5) transformer in a multi-task setting for QA, QG and answer extraction tasks using a Turkish QA dataset. |
Fatih Cagatay Akyon; Devrim Cavusoglu; Cemil Cengiz; Sinan Onur Altinuc; Alptekin Temizel; | arxiv-cs.LG | 2021-11-11 |
| 136 | Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose XLA-MAML, which performs direct cross-lingual adaption in the meta-learning stage. |
Qianying Liu; Fei Cheng; Sadao Kurohashi; | arxiv-cs.CL | 2021-11-10 |
| 137 | A Two-Stage Approach Towards Generalization in Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve this generalization, we introduce a KBQA framework based on a 2-stage architecture that explicitly separates semantic parsing from the knowledge base interaction, facilitating transfer learning across datasets and knowledge graphs. |
SRINIVAS RAVISHANKAR et. al. | arxiv-cs.CL | 2021-11-10 |
| 138 | Recent Advances in Automated Question Answering In Biomedical Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the basic methodologies used for developing general domain QA systems, followed by a thorough investigation of different aspects of biomedical QA systems, including benchmark datasets and several proposed approaches, both using structured databases and collection of texts. |
Krishanu Das Baksi; | arxiv-cs.AI | 2021-11-10 |
| 139 | Pre-trained Transformer-Based Approach for Arabic Question Answering : A Comparative Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we evaluate the state-of-the-art pre-trained transformers models for Arabic QA using four reading comprehension datasets which are Arabic-SQuAD, ARCD, AQAD, and TyDiQA-GoldP datasets. |
Kholoud Alsubhi; Amani Jamal; Areej Alhothali; | arxiv-cs.CL | 2021-11-10 |
| 140 | Reason First, Then Respond: Modular Generation for Knowledge-infused Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a modular model, Knowledge to Response (K2R), for incorporating knowledge into conversational agents, which breaks down this problem into two easier steps. |
Leonard Adolphs; Kurt Shuster; Jack Urbanek; Arthur Szlam; Jason Weston; | arxiv-cs.CL | 2021-11-09 |
| 141 | Visual Question Answering Based on Formal Logic Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a formal logic framework where (i) images are converted to logical background facts with the help of scene graphs, (ii) the questions are translated to first-order predicate logic clauses using a transformer based deep learning model, and (iii) perform satisfiability checks, by using the background knowledge and the grounding of predicate clauses, to obtain the answer. |
Muralikrishnna G. Sethuraman; Ali Payani; Faramarz Fekri; J. Clayton Kerce; | arxiv-cs.CV | 2021-11-08 |
| 142 | Ontology-based Question Answering Over Corporate Structured Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe the dialogue engine for a chat bot which can keep the conversation context and ask clarifying questions, simulating some aspects of the human logical thinking. |
Sergey Gorshkov; Constantin Kondratiev; Roman Shebalov; | arxiv-cs.SE | 2021-11-08 |
| 143 | Transformer Based Bengali Chatbot Using General Knowledge Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this research, we applied the transformer model for Bengali general knowledge chatbot based on the Bengali general knowledge Question Answer (QA) dataset. |
Abu Kaisar Mohammad Masum; Sheikh Abujar; Sharmin Akter; Nushrat Jahan Ria; Syed Akhter Hossain; | arxiv-cs.CL | 2021-11-06 |
| 144 | Case-based Reasoning for Natural Language Queries Over Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. |
RAJARSHI DAS et. al. | emnlp | 2021-11-05 |
| 145 | Improving Unsupervised Question Answering Via Summarization-Informed Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to overcome these shortcomings, we propose a distantly-supervised QG method which uses questions generated heuristically from summaries as a source of training data for a QG system. |
CHENYANG LYU et. al. | emnlp | 2021-11-05 |
| 146 | ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations. |
Rujun Han; Xiang Ren; Nanyun Peng; | emnlp | 2021-11-05 |
| 147 | Refocusing on Relevance: Personalization in NLG Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks. |
Shiran Dudy; Steven Bedrick; Bonnie Webber; | emnlp | 2021-11-05 |
| 148 | Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a model that explicitly handles multiple-entity questions by implementing a new intersection operation, which identifies the shared elements between two sets of entities. |
Priyanka Sen; Armin Oliya; Amir Saffari; | emnlp | 2021-11-05 |
| 149 | TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering Over Relation Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose TransferNet, an effective and transparent model for multi-hop QA, which supports both label and text relations in a unified framework. |
Jiaxin Shi; Shulin Cao; Lei Hou; Juanzi Li; Hanwang Zhang; | emnlp | 2021-11-05 |
| 150 | Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyze the redundancy present in encoded dense vectors and show that the default dimension of 768 is unnecessarily large. |
Xueguang Ma; Minghan Li; Kai Sun; Ji Xin; Jimmy Lin; | emnlp | 2021-11-05 |
| 151 | ReasonBERT: Pre-trained to Reason with Distant Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. |
XIANG DENG et. al. | emnlp | 2021-11-05 |
| 152 | Simple Entity-Centric Questions Challenge Dense Retrievers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate this issue and uncover that dense retrievers can only generalize to common entities unless the question pattern is explicitly observed during training. |
Christopher Sciavolino; Zexuan Zhong; Jinhyuk Lee; Danqi Chen; | emnlp | 2021-11-05 |
| 153 | Phrase Retrieval Learns Passage Retrieval, Too Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we follow the intuition that retrieving phrases naturally entails retrieving larger text blocks and study whether phrase retrieval can serve as the basis for coarse-level retrieval including passages and documents. |
Jinhyuk Lee; Alexander Wettig; Danqi Chen; | emnlp | 2021-11-05 |
| 154 | Explaining Answers with Entailment Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal, in the context of open-domain textual question-answering (QA), is to explain answers by showing the line of reasoning from what is known to the answer, rather than simply showing a fragment of textual evidence (a rationale). |
BHAVANA DALVI et. al. | emnlp | 2021-11-05 |
| 155 | Adaptive Information Seeking for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO. |
Yunchang Zhu; Liang Pang; Yanyan Lan; Huawei Shen; Xueqi Cheng; | emnlp | 2021-11-05 |
| 156 | Improving Query Graph Generation for Complex Question Answering Over Knowledge Base Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new solution to query graph generation that works in the opposite manner: we start with the entire knowledge base and gradually shrink it to the desired query graph. |
Kechen Qin; Cheng Li; Virgil Pavlu; Javed Aslam; | emnlp | 2021-11-05 |
| 157 | Large-Scale Relation Learning for Question Answering Over Knowledge Bases with Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge the gap between the natural language and the structured KB, we propose three relation learning tasks for BERT-based KBQA, including relation extraction, relation matching, and relation reasoning. |
YUANMENG YAN et. al. | emnlp | 2021-11-05 |
| 158 | SituatedQA: Incorporating Extra-Linguistic Contexts Into QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To study this challenge, we introduce SituatedQA, an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context. |
Michael Zhang; Eunsol Choi; | emnlp | 2021-11-05 |
| 159 | Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we are the first to use synthetic adversarial data generation to make question answering models more robust to human adversaries. |
MAX BARTOLO et. al. | emnlp | 2021-11-05 |
| 160 | Paired Examples As Indirect Supervision in Latent Decision Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a way to leverage paired examples that provide stronger cues for learning latent decisions. |
Nitish Gupta; Sameer Singh; Matt Gardner; Dan Roth; | emnlp | 2021-11-05 |
| 161 | What’s in A Name? Answer Equivalence For Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers). |
Chenglei Si; Chen Zhao; Jordan Boyd-Graber; | emnlp | 2021-11-05 |
| 162 | Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering Without Evidence Annotation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel approach (DistDR) that iteratively improves over a weak retriever by alternately finding evidence from the up-to-date model and encouraging the model to learn the most likely evidence. |
Chen Zhao; Chenyan Xiong; Jordan Boyd-Graber; Hal Daum? III; | emnlp | 2021-11-05 |
| 163 | Will This Question Be Answered? Question Filtering Via Answer Model Distillation for Efficient Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. |
Siddhant Garg; Alessandro Moschitti; | emnlp | 2021-11-05 |
| 164 | Structured Context and High-Coverage Grammar for Conversational Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new Logical Form (LF) grammar that can model a wide range of queries on the graph while remaining sufficiently simple to generate supervision data efficiently. |
Pierre Marion; Pawel Nowak; Francesco Piccinno; | emnlp | 2021-11-05 |
| 165 | QA-Align: Representing Cross-Text Content Overlap By Aligning Question-Answer Propositions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset. |
Daniela Brook Weiss; Paul Roit; Ayal Klein; Ori Ernst; Ido Dagan; | emnlp | 2021-11-05 |
| 166 | Editing Factual Knowledge in Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present KnowledgeEditor, a method which can be used to edit this knowledge and, thus, fix ‘bugs’ or unexpected predictions without the need for expensive re-training or fine-tuning. |
Nicola De Cao; Wilker Aziz; Ivan Titov; | emnlp | 2021-11-05 |
| 167 | Broaden The Vision: Geo-Diverse Visual Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we construct a Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test vision-and-language models’ ability to understand cultural and geo-location-specific commonsense. |
Da Yin; Liunian Harold Li; Ziniu Hu; Nanyun Peng; Kai-Wei Chang; | emnlp | 2021-11-05 |
| 168 | Topic Transferable Table Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we simulate the practical topic shift scenario by designing novel challenge benchmarks WikiSQL-TS and WikiTable-TS, consisting of train-dev-test splits in five distinct topic groups, based on the popular WikiSQL and WikiTable-Questions datasets. |
SANEEM CHEMMENGATH et. al. | emnlp | 2021-11-05 |
| 169 | Zero-Shot Dialogue State Tracking Via Cross-Task Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to transfer the cross-task knowledge from general question answering (QA) corpora for the zero-shot DST task. |
ZHAOJIANG LIN et. al. | emnlp | 2021-11-05 |
| 170 | Incorporating Medical Knowledge in BERT for Clinical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve these issues, in this research, we conduct a comprehensive examination of different techniques to add medical knowledge into a pre-trained BERT model for clinical relation extraction. |
Arpita Roy; Shimei Pan; | emnlp | 2021-11-05 |
| 171 | Measuring Association Between Labels and Free-Text Rationales IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate the extent to which the labels and rationales predicted by these models are associated, a necessary property of faithful explanation. |
Sarah Wiegreffe; Ana Marasovic; Noah A. Smith; | emnlp | 2021-11-05 |
| 172 | Generative Context Pair Selection for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts. |
DHEERU DUA et. al. | emnlp | 2021-11-05 |
| 173 | HittER: Hierarchical Transformers for Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity’s neighborhood. |
SANXING CHEN et. al. | emnlp | 2021-11-05 |
| 174 | What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we examine the behaviour of non-target heads, that is, the output of heads when given input that belongs to a different task than the one they were trained for. |
Mor Geva; Uri Katz; Aviv Ben-Arie; Jonathan Berant; | emnlp | 2021-11-05 |
| 175 | MLEC-QA: A Chinese Multi-Choice Biomedical Question Answering Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present MLEC-QA, the largest-scale Chinese multi-choice biomedical QA dataset, collected from the National Medical Licensing Examination in China. |
Jing Li; Shangping Zhong; Kaizhi Chen; | emnlp | 2021-11-05 |
| 176 | Multivalent Entailment Graphs for Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We make three contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies, like DEFEAT(Biden, Trump) entails WIN(Biden); (2) we actualize this theory by learning unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3) we demonstrate the capabilities of these graphs on a novel question answering task. |
NICK MCKENNA et. al. | emnlp | 2021-11-05 |
| 177 | Generating Self-Contained and Summary-Centric Question Answer Pairs Via Differentiable Reward Imitation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. |
Li Zhou; Kevin Small; Yong Zhang; Sandeep Atluri; | emnlp | 2021-11-05 |
| 178 | Foreseeing The Benefits of Incidental Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals. |
Hangfeng He; Mingyuan Zhang; Qiang Ning; Dan Roth; | emnlp | 2021-11-05 |
| 179 | Evaluation Paradigms in Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Question answering (QA) primarily descends from two branches of research: (1) Alan Turing’s investigation of machine intelligence at Manchester University and (2) Cyril Cleverdon’s comparison of library card catalog indices at Cranfield University. This position paper names and distinguishes these paradigms. |
Pedro Rodriguez; Jordan Boyd-Graber; | emnlp | 2021-11-05 |
| 180 | QuestEval: Summarization Asks for Fact-based Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we extend previous approaches and propose a unified framework, named QuestEval. |
THOMAS SCIALOM et. al. | emnlp | 2021-11-05 |
| 181 | Enhanced Language Representation with Label Knowledge for Span Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address those problems, we introduce a fresh paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations. |
Pan Yang; Xin Cong; Zhenyu Sun; Xingwu Liu; | emnlp | 2021-11-05 |
| 182 | Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce various ways to retrieve knowledge using text and images and two reader styles: classification and extraction. |
Man Luo; Yankai Zeng; Pratyay Banerjee; Chitta Baral; | emnlp | 2021-11-05 |
| 183 | Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method to improve the Cross-lingual Question Answering performance without requiring additional annotated data, leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. |
ARIJ RIABI et. al. | emnlp | 2021-11-05 |
| 184 | Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. |
MANUEL CIOSICI et. al. | emnlp | 2021-11-05 |
| 185 | End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we extend the boundaries of E2E learning for KGQA to include the training of an ER component. |
Amir Saffari; Armin Oliya; Priyanka Sen; Tom Ayoola; | emnlp | 2021-11-05 |
| 186 | CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a semi-automatic framework for generating disentangled shifts by introducing a controllable visual question-answer generation (VQAG) module that is capable of generating highly-relevant and diverse question-answer pairs with the desired dataset style. |
ARJUN AKULA et. al. | emnlp | 2021-11-05 |
| 187 | Neural Natural Logic Inference for Interpretable Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models. |
Jihao Shi; Xiao Ding; Li Du; Ting Liu; Bing Qin; | emnlp | 2021-11-05 |
| 188 | Powering Comparative Classification with Sentiment Analysis Via Domain Adaptive Knowledge Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC accuracy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. |
Zeyu Li; Yilong Qin; Zihan Liu; Wei Wang; | emnlp | 2021-11-05 |
| 189 | Cross-Policy Compliance Detection Via Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose to address policy compliance detection via decomposing it into question answering, where questions check whether the conditions stated in the policy apply to the scenario, and an expression tree combines the answers to obtain the label. |
Marzieh Saeidi; Majid Yazdani; Andreas Vlachos; | emnlp | 2021-11-05 |
| 190 | Toward Deconfounding The Effect of Entity Demographics for Question Answering Accuracy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: The goal of question answering (QA) is to answer _any_ question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, … |
Maharshi Gor; Kellie Webster; Jordan Boyd-Graber; | emnlp | 2021-11-05 |
| 191 | FewshotQA: A Simple Framework for Few-shot Learning of Question Answering Tasks Using Pre-trained Text-to-text Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose a simple fine-tuning framework that leverages pre-trained text-to-text models and is directly aligned with their pre-training framework. |
Rakesh Chada; Pradeep Natarajan; | emnlp | 2021-11-05 |
| 192 | ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose ARMAN, a Transformer-based encoder-decoder model pre-trained with three novel objectives to address this issue. |
Alireza Salemi; Emad Kebriaei; Ghazal Neisi Minaei; Azadeh Shakery; | emnlp | 2021-11-05 |
| 193 | Contrastive Domain Adaptation for Question Answering Using Limited Text Corpora Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework for domain adaptation called contrastive domain adaptation for QA (CAQA). |
Zhenrui Yue; Bernhard Kratzwald; Stefan Feuerriegel; | emnlp | 2021-11-05 |
| 194 | Joint Passage Ranking for Diverse Multi-Answer Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce JPR, a joint passage retrieval model focusing on reranking. |
Sewon Min; Kenton Lee; Ming-Wei Chang; Kristina Toutanova; Hannaneh Hajishirzi; | emnlp | 2021-11-05 |
| 195 | Narrative Embedding: Re-Contextualization Through Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel approach for narrative event representation using attention to re-contextualize events across the whole story. |
Sean Wilner; Daniel Woolridge; Madeleine Glick; | emnlp | 2021-11-05 |
| 196 | FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop FiD-Ex, which addresses these shortcomings for seq2seq models by: 1) introducing sentence markers to eliminate explanation fabrication by encouraging extractive generation, 2) using the fusion-in-decoder architecture to handle long input contexts, and 3) intermediate fine-tuning on re-structured open domain QA datasets to improve few-shot performance. |
KUSHAL LAKHOTIA et. al. | emnlp | 2021-11-05 |
| 197 | Discovering The Unknown Knowns: Turning Implicit Knowledge in The Dataset Into Explicit Training Examples for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Building upon these insights, we present a simple data augmentation pipeline SimpleAug to turn this known knowledge into training examples for VQA. |
Jihyung Kil; Cheng Zhang; Dong Xuan; Wei-Lun Chao; | emnlp | 2021-11-05 |
| 198 | Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a new parameterization of set-valued retrieval that handles unanswerable queries, and we show that marginalizing over this set during training allows a model to mitigate false negatives in supporting evidence annotations. |
Ansong Ni; Matt Gardner; Pradeep Dasigi; | emnlp | 2021-11-05 |
| 199 | Single-dataset Experts for Multi-dataset Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our approach is to model multi-dataset question answering with an ensemble of single-dataset experts, by training a collection of lightweight, dataset-specific adapter modules (Houlsby et al., 2019) that share an underlying Transformer model. |
Dan Friedman; Ben Dodge; Danqi Chen; | emnlp | 2021-11-05 |
| 200 | Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues Via Question Generation and Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by recent work on evaluating factual consistency in abstractive summarization, we propose an automatic evaluation metric for factual consistency in knowledge-grounded dialogue using automatic question generation and question answering. |
OR HONOVICH et. al. | emnlp | 2021-11-05 |
| 201 | Grounded Graph Decoding Improves Compositional Generalization in Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Grounded Graph Decoding, a method to improve compositional generalization of language representations by grounding structured predictions with an attention mechanism. |
YU GAI et. al. | arxiv-cs.CL | 2021-11-05 |
| 202 | FinQA: A Dataset of Numerical Reasoning Over Financial Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. |
ZHIYU CHEN et. al. | emnlp | 2021-11-05 |
| 203 | MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). |
ZEWEN CHI et. al. | emnlp | 2021-11-05 |
| 204 | Medicines Question Answering System, MeQA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present the first system in Spanish capable of answering questions about medicines for human use, called MeQA (Medicines Question Answering), a project created by the Spanish Agency for Medicines and Health Products (AEMPS, for its acronym in Spanish). |
Jesús Santamaría; | arxiv-cs.CL | 2021-11-04 |
| 205 | Reducing The Impact of Out of Vocabulary Words in The Translation of Natural Language Questions Into SPARQL Queries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we combine Named Entity Linking, Named Entity Recognition, and Neural Machine Translation to perform automatic translation of natural language questions into SPARQL queries. |
Manuel A. Borroto Santana; Francesco Ricca; Bernardo Cuteri; | arxiv-cs.CL | 2021-11-04 |
| 206 | SERC: Syntactic and Semantic Sequence Based Event Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: So we propose a joint model that incorporates both temporal and causal features to perform causal relation classification. |
Kritika Venkatachalam; Raghava Mutharaju; Sumit Bhatia; | arxiv-cs.CL | 2021-11-03 |
| 207 | UQuAD1.0: Development of An Urdu Question Answering Training Data for Machine Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we used two types of MRC models: rule-based baseline and advanced Transformer-based models. |
Samreen Kazi; Shakeel Khoja; | arxiv-cs.CL | 2021-11-02 |
| 208 | ICallee: Recovering Call Graphs for Binaries IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new solution iCallee based on the Siamese Neural Network, inspired by the advances in question-answering applications. |
WENYU ZHU et. al. | arxiv-cs.SE | 2021-11-02 |
| 209 | Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel paradigm that enables scalable data collection targeting the comprehension of news documents, viewing these questions through the lens of discourse. |
WEI-JEN KO et. al. | arxiv-cs.CL | 2021-11-01 |
| 210 | Enhanced Language Representation with Label Knowledge for Span Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance. |
Pan Yang; Xin Cong; Zhenyun Sun; Xingwu Liu; | arxiv-cs.CL | 2021-11-01 |
| 211 | DSC-IITISM at FinCausal 2021: Combining POS Tagging with Attention-based Contextual Representations for Identifying Causal Relationships in Financial Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we explore several methods to identify and extract cause-effect pairs in financial documents using transformers. |
Gunjan Haldar; Aman Mittal; Pradyumna Gupta; | arxiv-cs.CL | 2021-10-31 |
| 212 | Text Classification for Task-based Source Code Related Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a two-fold deep learning model: Seq2Seq and a binary classifier that takes in the intent (which is in natural language) and code snippets in Python. |
Sairamvinay Vijayaraghavan; Jinxiao Song; David Tomassi; Siddhartha Punj; Jailan Sabet; | arxiv-cs.SE | 2021-10-31 |
| 213 | Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Path and Knowledge Embedding-Enhanced multi-relational Question Answering model (PKEEQA), which leverages multi-hop paths between entities in the KG to evaluate the ambipolar correlation between a path embedding and a multi-relational question embedding via a customizable path representation mechanism, benefiting for achieving more accurate answers from the perspective of both the triple facts and the extra paths. |
GUANGLIN NIU et. al. | arxiv-cs.CL | 2021-10-29 |
| 214 | Learning Representations for Zero-Shot Retrieval Over Structured Data IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our proposed method, analogues to a passage retrieval model in traditional Question-Answering systems, describes an architecture to discern the correct table pertaining to a given query from amongst a large pool of candidate tables. |
Harsh Kohli; | arxiv-cs.IR | 2021-10-29 |
| 215 | Dense Hierarchical Retrieval for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework that can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage. |
YE LIU et. al. | arxiv-cs.IR | 2021-10-28 |
| 216 | What Makes Us Curious? Analysis of A Corpus of Open-domain Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes what information we extracted from the automated analysis of the WTC corpus of open-domain questions. |
Zhaozhen Xu; Amelia Howarth; Nicole Briggs; Nello Cristianini; | arxiv-cs.CL | 2021-10-28 |
| 217 | Multi-stage Clarification in Conversational AI: The Case of Question-Answering Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a multi-stage clarification mechanism for prompting clarification and query selection in the context of a question answering dialogue system. |
Hadrien Lautraite; Nada Naji; Louis Marceau; Marc Queudot; Eric Charton; | arxiv-cs.CL | 2021-10-28 |
| 218 | Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Relation Extractor-Reader and Comparator (RERC), a three-stage framework based on complex question decomposition, which is the first work that the RERC model has been proposed and applied in solving the multi-hop QA challenges. |
Ruiliu Fu; Han Wang; Xuejun Zhang; Jun Zhou; Yonghong Yan; | arxiv-cs.CL | 2021-10-26 |
| 219 | Vector-valued Distance and Gyrocalculus on The Space of Symmetric Positive Definite Matrices Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the use of the vector-valued distance to compute distances and extract geometric information from the manifold of symmetric positive definite matrices (SPD), and develop gyrovector calculus, constructing analogs of vector space operations in this curved space. |
Federico López; Beatrice Pozzetti; Steve Trettel; Michael Strube; Anna Wienhard; | arxiv-cs.LG | 2021-10-26 |
| 220 | IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a new challenge of Icon Question Answering (IconQA) with the goal of answering a question in an icon image context. |
PAN LU et. al. | arxiv-cs.CV | 2021-10-25 |
| 221 | Improving Embedded Knowledge Graph Multi-hop Question Answering By Introducing Relational Chain Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues in multi-hop KGQA, we proposea novel model in this paper, namely Relational Chain-based Embed-ded KGQA (Rce-KGQA), which simultaneously utilizes the explicitrelational chain described in natural language questions and the implicit relational chain stored in structured KG. |
Weiqiang Jin; Hang Yu; Xi Tao; Ruiping Yin; | arxiv-cs.CL | 2021-10-25 |
| 222 | Transferring Domain-Agnostic Knowledge in Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate a transfer learning method by the introduction of domain-agnostic knowledge and domain-specific knowledge. Second, we construct a new VideoQA dataset with 21,412 human-generated question-answer samples for comparable transfer of knowledge. |
TIANRAN WU et. al. | arxiv-cs.CV | 2021-10-25 |
| 223 | ListReader: Extracting List-form Answers for Opinion Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, this paper proposes ListReader, a neural ex-tractive QA model for list-form answer. |
Peng Cui; Dongyao Hu; Le Hu; | arxiv-cs.CL | 2021-10-22 |
| 224 | Single-Modal Entropy Based Active Learning for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address Active Learning in the multi-modal setting of Visual Question Answering (VQA). |
Dong-Jin Kim; Jae Won Cho; Jinsoo Choi; Yunjae Jung; In So Kweon; | arxiv-cs.CV | 2021-10-21 |
| 225 | DEEPAGÉ: Answering Questions in Portuguese About The Brazilian Environment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce multiple QA systems that combine in novel ways the BM25 algorithm, a sparse retrieval technique, with PTT5, a pre-trained state-of-the-art language model. |
FLÁVIO NAKASATO CAÇÃO et. al. | arxiv-cs.CL | 2021-10-19 |
| 226 | Ensemble ALBERT on SQuAD 2.0 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In our Paper, we utilized the fine-tuned ALBERT models and implemented combinations of additional layers (e.g. attention layer, RNN layer) on top of them to improve model performance on Stanford Question Answering Dataset (SQuAD 2.0). |
Shilun Li; Renee Li; Veronica Peng; | arxiv-cs.CL | 2021-10-18 |
| 227 | Ranking Facts for Explaining Answers to Elementary Science Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our aim is to obtain better matches for valid facts of an explanation for the correct answer of a question over the available fact candidates. |
Jennifer D’Souza; Isaiah Onando Mulang’; Soeren Auer; | arxiv-cs.CL | 2021-10-18 |
| 228 | Explore Before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the human ability to think twice before moving and conceive several feasible paths to seek a goal in unfamiliar scenes, we present a route planning method named Path Estimation and Memory Recalling (PEMR) framework. |
Yang Wu; Shirui Feng; Guanbin Li; Liang Lin; | arxiv-cs.CV | 2021-10-16 |
| 229 | Pro-KD: Progressive Distillation By Following The Footsteps of The Teacher Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenging problems, in this work, we introduce our progressive knowledge distillation (Pro-KD) technique which defines a smoother training path for the student by following the training footprints of the teacher instead of solely relying on distilling from a single mature fully-trained teacher. |
MEHDI REZAGHOLIZADEH et. al. | arxiv-cs.CL | 2021-10-16 |
| 230 | Open Domain Question Answering Over Virtual Documents: A Unified Approach for Data and Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to bridge this gap and use the data-to-text method as a means for encoding structured knowledge for knowledge-intensive applications, i.e. open-domain question answering (QA). |
Kaixin Ma; Hao Cheng; Xiaodong Liu; Eric Nyberg; Jianfeng Gao; | arxiv-cs.CL | 2021-10-15 |
| 231 | MixQG: Neural Question Generation with Mixed Answer Types Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a neural question generator, MixQG, to bridge this gap. |
Lidiya Murakhovs’ka; Chien-Sheng Wu; Tong Niu; Wenhao Liu; Caiming Xiong; | arxiv-cs.CL | 2021-10-15 |
| 232 | BBQ: A Hand-Built Bias Benchmark for Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the Bias Benchmark for QA (BBQ), a dataset consisting of question-sets constructed by the authors that highlight \textit{attested} social biases against people belonging to protected classes along nine different social dimensions relevant for U.S. English-speaking contexts. |
ALICIA PARRISH et. al. | arxiv-cs.CL | 2021-10-15 |
| 233 | ContraQA: Question Answering Under Contradicting Contexts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the risk of misinformation to QA models by investigating the behavior of the QA model under contradicting contexts that are mixed with both real and fake information. We create the first large-scale dataset for this problem, namely Contra-QA, which contains over 10K human-written and model-generated contradicting pairs of contexts. |
Liangming Pan; Wenhu Chen; Min-Yen Kan; William Yang Wang; | arxiv-cs.CL | 2021-10-14 |
| 234 | CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Consequently, we propose a novel QA dataset based on the Common Crawl project in this paper. |
PATRICK HUBER et. al. | arxiv-cs.CL | 2021-10-14 |
| 235 | Cross-Lingual GenQA: A Language-Agnostic Generative Question Answering Approach for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the first generalization of the GenQA approach for the multilingual environment. To this end, we present the GenTyDiQA dataset, which extends the TyDiQA evaluation data (Clark et al., 2020) with natural-sounding, well-formed answers in Arabic, Bengali, English, Japanese, and Russian. |
Benjamin Muller; Luca Soldaini; Rik Koncel-Kedziorski; Eric Lind; Alessandro Moschitti; | arxiv-cs.CL | 2021-10-14 |
| 236 | Retrieval-guided Counterfactual Generation for QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we develop a Retrieve-Generate-Filter(RGF) technique to create counterfactual evaluation and training data with minimal human supervision. |
Bhargavi Paranjape; Matthew Lamm; Ian Tenney; | arxiv-cs.CL | 2021-10-14 |
| 237 | ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply. |
Haitian Sun; William W. Cohen; Ruslan Salakhutdinov; | arxiv-cs.CL | 2021-10-13 |
| 238 | Open-Domain Question-Answering for COVID-19 and Other Emergent Domains Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present such a system for the emergent domain of COVID-19. |
Sharon Levy; Kevin Mo; Wenhan Xiong; William Yang Wang; | arxiv-cs.CL | 2021-10-13 |
| 239 | Improving Users’ Mental Model with Attention-directed Counterfactual Edits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that showing controlled counterfactual image-question examples are more effective at improving the mental model of users as compared to simply showing random examples. |
KAMRAN ALIPOUR et. al. | arxiv-cs.CV | 2021-10-13 |
| 240 | Salient Phrase Aware Dense Retrieval: Can A Dense Retriever Imitate A Sparse One? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we show that a dense retriever {\Lambda} can be trained to imitate a sparse one, and SPAR is built by augmenting a standard dense retriever with {\Lambda}. |
XILUN CHEN et. al. | arxiv-cs.CL | 2021-10-13 |
| 241 | Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an end-to-end neural complexity-controllable question generation model, which incorporates a mixture of experts (MoE) as the selector of soft templates to improve the accuracy of complexity control and the quality of generated questions. |
SHENG BI et. al. | arxiv-cs.CL | 2021-10-13 |
| 242 | Attention-guided Generative Models for Extractive Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel method for applying Transformer models to extractive question answering (QA) tasks. |
Peng Xu; Davis Liang; Zhiheng Huang; Bing Xiang; | arxiv-cs.CL | 2021-10-12 |
| 243 | MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a new dataset, MMIU (MultiModal Intent Understanding), that contains questions and corresponding intents provided by human annotators while looking at images. |
ALKESH PATEL et. al. | arxiv-cs.CV | 2021-10-12 |
| 244 | Mention Memory: Incorporating Textual Knowledge Into Transformers Through Entity Mention Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to address this problem by integrating a semi-parametric representation of a large text corpus into a Transformer model as a source of factual knowledge. |
Michiel de Jong; Yury Zemlyanskiy; Nicholas FitzGerald; Fei Sha; William Cohen; | arxiv-cs.CL | 2021-10-12 |
| 245 | Winning The ICCV’2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This technical report describes our winning strategies for the VALUE challenge: 1) single model optimization, 2) transfer learning with visual concepts, and 3) task-aware ensemble. |
MINCHUL SHIN et. al. | arxiv-cs.CV | 2021-10-12 |
| 246 | Pano-AVQA: Grounded Audio-Visual Question Answering on 360$^\circ$ Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel benchmark named Pano-AVQA as a large-scale grounded audio-visual question answering dataset on panoramic videos. |
Heeseung Yun; Youngjae Yu; Wonsuk Yang; Kangil Lee; Gunhee Kim; | arxiv-cs.CV | 2021-10-11 |
| 247 | Explainable Fact-checking Through Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. |
Jing Yang; Didier Vega-Oliveros; Taís Seibt; Anderson Rocha; | arxiv-cs.CL | 2021-10-11 |
| 248 | Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a browser-based benchmarking tool for researchers and challenge organizers, with an API for easy integration of new models and datasets to keep up with the fast-changing landscape of VQA. |
Dirk Väth; Pascal Tilli; Ngoc Thang Vu; | arxiv-cs.CV | 2021-10-11 |
| 249 | AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the data sparsity issue, we propose AskMe, which aims to leverage the rich, hybrid behavior interactions in CQA to improve the question recommendation performance. |
NUO LI et. al. | arxiv-cs.IR | 2021-10-11 |
| 250 | Enhance Long Text Understanding Via Distilled Gist Detector from Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider the problem of how to disentangle the gist-relevant and irrelevant information for long text understanding. |
Yan Liu; Yazheng Yang; | arxiv-cs.CL | 2021-10-10 |
| 251 | What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here for the first time, we introduce a dataset of semantic relatedness for sentence pairs. |
Mohamed Abdalla; Krishnapriya Vishnubhotla; Saif M. Mohammad; | arxiv-cs.CL | 2021-10-10 |
| 252 | Distantly-Supervised Evidence Retrieval Enables Question Answering Without Evidence Annotation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel approach (DistDR) that iteratively improves over a weak retriever by alternately finding evidence from the up-to-date model and encouraging the model to learn the most likely evidence. |
Chen Zhao; Chenyan Xiong; Jordan Boyd-Graber; Hal Daumé III; | arxiv-cs.CL | 2021-10-10 |
| 253 | A Framework for Rationale Extraction for Deep QA Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we use `Integrated Gradients’ to extract rationale for existing state-of-the-art models in the task of Reading Comprehension based Question Answering (RCQA). |
Sahana Ramnath; Preksha Nema; Deep Sahni; Mitesh M. Khapra; | arxiv-cs.CL | 2021-10-09 |
| 254 | On The Hidden Treasure of Dialog in Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a new approach to understand the whole story without such external sources. |
Deniz Engin; Francois Schnitzler; Ngoc Q. K. Duong; Yannis Avrithis; | iccv | 2021-10-08 |
| 255 | Auto-Parsing Network for Image Captioning and Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an Auto-Parsing Network (APN) to discover and exploit the input data’s hidden tree structures for improving the effectiveness of the Transformer-based vision-language systems. |
Xu Yang; Chongyang Gao; Hanwang Zhang; Jianfei Cai; | iccv | 2021-10-08 |
| 256 | Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the fact that humans can recognize novel concepts by composing existed concepts and capsule network’s ability of representing part-whole hierarchies, we propose to use capsules to represent parts and introduce "Linguistically Routing" to merge parts with human-prior hierarchies. |
Qingxing Cao; Wentao Wan; Keze Wang; Xiaodan Liang; Liang Lin; | iccv | 2021-10-08 |
| 257 | Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the few-shot visual-semantic learning and present the Hierarchical Graph ATtention network (HGAT). |
CHENGXIANG YIN et. al. | iccv | 2021-10-08 |
| 258 | Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a Multimodal Co-Attention Transformer (MCAT) framework that learns an interpretable, dense co-attention mapping between WSIs and genomic features formulated in an embedding space. |
RICHARD J. CHEN et. al. | iccv | 2021-10-08 |
| 259 | Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce an evaluation methodology for visual question answering (VQA) to better diagnose cases of shortcut learning. |
Corentin Dancette; Remi Cadene; Damien Teney; Matthieu Cord; | iccv | 2021-10-08 |
| 260 | MDETR – Modulated Detection for End-to-End Multi-Modal Understanding IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. |
AISHWARYA KAMATH et. al. | iccv | 2021-10-08 |
| 261 | Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To equip AI system with the ability to understand dynamic ENVironments, we build a video Question Answering dataset named Env-QA. |
Difei Gao; Ruiping Wang; Ziyi Bai; Xilin Chen; | iccv | 2021-10-08 |
| 262 | Contrast and Classify: Training Robust VQA Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel training paradigm (ConClaT) that optimizes both cross-entropy and contrastive losses. |
Yash Kant; Abhinav Moudgil; Dhruv Batra; Devi Parikh; Harsh Agrawal; | iccv | 2021-10-08 |
| 263 | Weakly Supervised Relative Spatial Reasoning for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this, we design two objectives as proxies for 3D spatial reasoning (SR) — object centroid estimation, and relative position estimation, and train V&L with weak supervision from off-the-shelf depth estimators. |
Pratyay Banerjee; Tejas Gokhale; Yezhou Yang; Chitta Baral; | iccv | 2021-10-08 |
| 264 | KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel method KG-FiD, which filters noisy passages by leveraging the structural relationship among the retrieved passages with a knowledge graph. |
DONGHAN YU et. al. | arxiv-cs.CL | 2021-10-08 |
| 265 | TRAR: Routing The Attention Spans in Transformer for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an example-dependent routing scheme called TRAnsformer Routing (TRAR) to address this issue. |
YIYI ZHOU et. al. | iccv | 2021-10-08 |
| 266 | Video Question Answering Using Language-Guided Deep Compressed-Domain Video Feature Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a novel deep neural network to provide video QA features obtained from coded video bit-stream to reduce the complexity. |
Nayoung Kim; Seong Jong Ha; Je-Won Kang; | iccv | 2021-10-08 |
| 267 | Just Ask: Learning To Answer Questions From Millions of Narrated Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision. |
Antoine Yang; Antoine Miech; Josef Sivic; Ivan Laptev; Cordelia Schmid; | iccv | 2021-10-08 |
| 268 | Greedy Gradient Ensemble for Robust Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We further propose a new de-bias framework, Greedy Gradient Ensemble (GGE), which combines multiple biased models for unbiased base model learning. |
Xinzhe Han; Shuhui Wang; Chi Su; Qingming Huang; Qi Tian; | iccv | 2021-10-08 |
| 269 | Unshuffling Data for Improved Generalization in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a training procedure to capture the patterns that are stable across environments while discarding spurious ones. |
Damien Teney; Ehsan Abbasnejad; Anton van den Hengel; | iccv | 2021-10-08 |
| 270 | HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a Hierarchical VisuAl-Semantic RelatIonal Reasoning (HAIR) framework to address these limitations. |
Fei Liu; Jing Liu; Weining Wang; Hanqing Lu; | iccv | 2021-10-08 |
| 271 | A Few More Examples May Be Worth Billions of Parameters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks. |
Yuval Kirstain; Patrick Lewis; Sebastian Riedel; Omer Levy; | arxiv-cs.CL | 2021-10-08 |
| 272 | Pano-AVQA: Grounded Audio-Visual Question Answering on 360deg Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel benchmark named Pano-AVQA as a large-scale grounded audio-visual question answering dataset on panoramic videos. |
Heeseung Yun; Youngjae Yu; Wonsuk Yang; Kangil Lee; Gunhee Kim; | iccv | 2021-10-08 |
| 273 | GNN Is A Counter? Revisiting GNN for Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We discover that even a very simple graph neural counter can outperform all the existing GNN modules on CommonsenseQA and OpenBookQA, two popular QA benchmark datasets which heavily rely on knowledge-aware reasoning. |
Kuan Wang; Yuyu Zhang; Diyi Yang; Le Song; Tao Qin; | arxiv-cs.AI | 2021-10-07 |
| 274 | Multi-tasking Dialogue Comprehension with Discourse Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To accurately perform the question-answering (QA) task according to such multi-party dialogue, models have to handle fundamentally different discourse relationships from common non-dialogue plain text, where discourse relations are supposed to connect two far apart utterances in a linguistics-motivated way.To further explore the role of such unusual discourse structure on the correlated QA task in terms of MRC, we propose the first multi-task model for jointly performing QA and discourse parsing (DP) on the multi-party dialogue MRC task. |
Yuchen He; Zhuosheng Zhang; Hai Zhao; | arxiv-cs.CL | 2021-10-07 |
| 275 | Noisy Text Data: Achilles’ Heel of Popular Transformer Based NLP Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the open question by Kumar et al. (2020) to explore the sensitivity of popular transformer based NLP models to noise in the text data. |
Kartikay Bagla; Ankit Kumar; Shivam Gupta; Anuj Gupta; | arxiv-cs.CL | 2021-10-07 |
| 276 | How BPE Affects Memorization in Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate that the size of the subword vocabulary learned by Byte-Pair Encoding (BPE) greatly affects both ability and tendency of standard Transformer models to memorize training data, even when we control for the number of learned parameters. |
Eugene Kharitonov; Marco Baroni; Dieuwke Hupkes; | arxiv-cs.CL | 2021-10-06 |
| 277 | Coarse-to-Fine Reasoning for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new reasoning framework to fill the gap between visual features and semantic clues in the VQA task. |
BINH X. NGUYEN et. al. | arxiv-cs.CV | 2021-10-06 |
| 278 | A Comparative Study of Transformer-Based Language Models on Extractive Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we train various pre-trained language models and fine-tune them on multiple question answering datasets of varying levels of difficulty to determine which of the models are capable of generalizing the most comprehensively across different datasets. |
Kate Pearce; Tiffany Zhan; Aneesh Komanduri; Justin Zhan; | arxiv-cs.CL | 2021-10-06 |
| 279 | EntQA: Entity Linking As Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new model that does not suffer from this limitation called EntQA, which stands for Entity linking as Question Answering. |
Wenzheng Zhang; Wenyue Hua; Karl Stratos; | arxiv-cs.CL | 2021-10-05 |
| 280 | Encoder Adaptation of Dense Passage Retrieval for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, in this paper, we want to know how an in-distribution (IND) question/passage encoder would generalize if paired with an OOD passage/question encoder from another domain. |
Minghan Li; Jimmy Lin; | arxiv-cs.IR | 2021-10-04 |
| 281 | Perhaps PTLMs Should Go to School — A Task to Assess Open Book and Closed Book QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. |
MANUEL R. CIOSICI et. al. | arxiv-cs.CL | 2021-10-04 |
| 282 | Counterfactual Samples Synthesizing and Training for Robust Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel model-agnostic Counterfactual Samples Synthesizing and Training (CSST) strategy. |
Long Chen; Yuhang Zheng; Yulei Niu; Hanwang Zhang; Jun Xiao; | arxiv-cs.CV | 2021-10-03 |
| 283 | Generating User-Centred Explanations Via Illocutionary Question Answering: From Philosophy to Interfaces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new method for generating explanations with AI and a tool to test its expressive power within a user interface. |
Francesco Sovrano; Fabio Vitali; | arxiv-cs.HC | 2021-10-02 |
| 284 | TopiOCQA: Open-domain Conversational Question Answering with Topic Switching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce TopiOCQA (pronounced Tapioca), an open-domain conversational dataset with topic switches on Wikipedia. |
Vaibhav Adlakha; Shehzaad Dhuliawala; Kaheer Suleman; Harm de Vries; Siva Reddy; | arxiv-cs.CL | 2021-10-02 |
| 285 | Asking Questions on Handwritten Document Collections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Datasets presented in this work are available to download at docvqa.org |
Minesh Mathew; Lluis Gomez; Dimosthenis Karatzas; CV Jawahar; | arxiv-cs.CV | 2021-10-01 |
| 286 | Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus primarily on UCCA graph-based semantic representation. |
Necva Bölücü; Burcu Can; | arxiv-cs.CL | 2021-10-01 |
| 287 | Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To facilitate a more personalized interaction with the policies, in this work, we propose an automated privacy policy question answering assistant that extracts a summary in response to the input user query. |
Moniba Keymanesh; Micha Elsner; Srinivasan Parthasarathy; | arxiv-cs.CL | 2021-09-29 |
| 288 | Text Simplification for Comprehension-based Question-Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the effect of text simplification in the task of question-answering using a comprehension context. |
Tanvi Dadu; Kartikey Pant; Seema Nagar; Ferdous Ahmed Barbhuiya; Kuntal Dey; | arxiv-cs.CL | 2021-09-28 |
| 289 | Multimodal Integration of Human-Like Attention in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the Multimodal Human-like Attention Network (MULAN) – the first method for multimodal integration of human-like attention on image and text during training of VQA models. |
EKTA SOOD et. al. | arxiv-cs.CV | 2021-09-27 |
| 290 | An Empirical Study of Question Discussions on Stack Overflow Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct an empirical study that focuses on the nature of question discussions. |
Wenhan Zhu; Haoxiang Zhang; Ahmed E. Hassan; Michael W. Godfrey; | arxiv-cs.SE | 2021-09-27 |
| 291 | FQuAD2.0: French Question Answering and Knowing That You Know Nothing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the present work, we introduce FQuAD2.0, which extends FQuAD with 17,000+ unanswerable questions, annotated adversarially, in order to be similar to answerable ones. |
Quentin Heinrich; Gautier Viaud; Wacim Belblidia; | arxiv-cs.CL | 2021-09-27 |
| 292 | Context-guided Triple Matching for Multiple Choice Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this issue, this paper introduces a novel Context-guided Triple Matching algorithm, which is achieved by integrating a Triple Matching (TM) module and a Contrastive Regularization (CR). |
XUN YAO et. al. | arxiv-cs.CL | 2021-09-27 |
| 293 | SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, wepresent SYGMA, a modular approach facilitating general-izability across multiple knowledge bases and multiple rea-soning types. |
SUMIT NEELAM et. al. | arxiv-cs.CL | 2021-09-27 |
| 294 | VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present VQA-MHUG – a novel 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA) collected using a high-speed eye tracker. |
Ekta Sood; Fabian Kögel; Florian Strohm; Prajit Dhar; Andreas Bulling; | arxiv-cs.CV | 2021-09-27 |
| 295 | Improving Question Answering Performance Using Knowledge Distillation and Active Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this manuscript, we conduct a comprehensive analysis of the mentioned challenges and introduce suitable countermeasures. |
Yasaman Boreshban; Seyed Morteza Mirbostani; Gholamreza Ghassem-Sani; Seyed Abolghasem Mirroshandel; Shahin Amiriparian; | arxiv-cs.CL | 2021-09-26 |
| 296 | More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we survey 47 recent textual QA benchmark datasets and propose a new taxonomy from an application point of view. |
Yang Bai; Daisy Zhe Wang; | arxiv-cs.CL | 2021-09-24 |
| 297 | How to Find A Good Image-text Embedding for Remote Sensing Visual Question Answering? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study three different fusion methodologies in the context of VQA for remote sensing and analyse the gains in accuracy with respect to the model complexity. |
Christel Chappuis; Sylvain Lobry; Benjamin Kellenberger; Bertrand Le Saux; Devis Tuia; | arxiv-cs.CV | 2021-09-24 |
| 298 | SD-QA: Spoken Dialectal Question Answering for The Real World Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance. |
Fahim Faisal; Sharlina Keshava; Md Mahfuz ibn Alam; Antonios Anastasopoulos; | arxiv-cs.CL | 2021-09-24 |
| 299 | Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we investigate the capabilities of multilingually pre-trained language models on cross-lingual QA. |
Fahim Faisal; Antonios Anastasopoulos; | arxiv-cs.CL | 2021-09-24 |
| 300 | ParaShoot: A Hebrew Question Answering Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present ParaShoot, the first question answering dataset in modern Hebrew. |
Omri Keren; Omer Levy; | arxiv-cs.CL | 2021-09-23 |
| 301 | Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we use a synonym replacement-based approach to augment questions with low lexical overlap. |
Kazutoshi Shinoda; Saku Sugawara; Akiko Aizawa; | arxiv-cs.CL | 2021-09-23 |
| 302 | BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce BiRdQA, a bilingual multiple-choice question answering dataset with 6614 English riddles and 8751 Chinese riddles. |
Yunxiang Zhang; Xiaojun Wan; | arxiv-cs.CL | 2021-09-22 |
| 303 | Towards Universal Dense Retrieval for Open-domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate dense retrieval models in the context of open-domain question answering across different input distributions. |
Christopher Sciavolino; | arxiv-cs.CL | 2021-09-22 |
| 304 | K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose K-AID, a systematic approach that includes a low-cost knowledge acquisition process for acquiring domain knowledge, an effective knowledge infusion module for improving model performance, and a knowledge distillation component for reducing the model size and deploying K-PLMs on resource-restricted devices (e.g., CPU) for real-world application. |
FU SUN et. al. | arxiv-cs.AI | 2021-09-22 |
| 305 | NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above shortcomings, we introduce NOAHQA, a conversational and bilingual QA dataset with questions requiring numerical reasoning with compound mathematical expressions. |
QIYUAN ZHANG et. al. | arxiv-cs.CL | 2021-09-22 |
| 306 | Recursively Summarizing Books with Human Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present progress on this problem on the task of abstractive summarization of entire fiction novels. We release datasets of samples from our model. |
JEFF WU et. al. | arxiv-cs.CL | 2021-09-22 |
| 307 | What Would It Take to Get Biomedical QA Systems Into Practice? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we discuss a set of criteria that, if met, we argue would likely increase the utility of biomedical QA systems, which may in turn lead to adoption of such systems in practice. |
Gregory Kell; Iain J. Marshall; Byron C. Wallace; Andre Jaun; | arxiv-cs.CL | 2021-09-21 |
| 308 | Blindness to Modality Helps Entailment Graph Mining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We build Entailment Graphs using a news corpus filtered with a modality parser, and show that stripping modal modifiers from predicates in fact increases performance. |
Liane Guillou; Sander Bijl de Vroe; Mark Johnson; Mark Steedman; | arxiv-cs.CL | 2021-09-21 |
| 309 | Relation-Guided Pre-Training for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy this problem, in this paper, we propose a Relation-Guided Pre-Training (RGPT-QA) framework. We first generate a relational QA dataset covering a wide range of relations from both the Wikidata triplets and Wikipedia hyperlinks. |
Ziniu Hu; Yizhou Sun; Kai-Wei Chang; | arxiv-cs.CL | 2021-09-21 |
| 310 | A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in The OBQA Context Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a simple yet effective framework to address these problems by jointly ranking passages and selecting sentences. |
Man Luo; Shuguang Chen; Chitta Baral; | arxiv-cs.CL | 2021-09-21 |
| 311 | Does Vision-and-Language Pretraining Improve Lexical Grounding? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We find that the multimodal models fail to significantly outperform the text-only variants, suggesting that future work is required if multimodal pretraining is to be pursued as a means of improving NLP in general. |
Tian Yun; Chen Sun; Ellie Pavlick; | arxiv-cs.CL | 2021-09-21 |
| 312 | Spectral and Energy Efficiency of Multicell Massive MIMO With Variable-Resolution ADCs Over Correlated Rayleigh Fading Channels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper analyzes the performance of multicell massive multiple-input and multiple-output (MIMO) systems with variable-resolution analog-to-digital converters (ADCs). |
Youzhi Xiong; Sanshan Sun; Ning Wei; Li Liu; Zhongpei Zhang; | arxiv-cs.IT | 2021-09-20 |
| 313 | Modality and Negation in Event Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an open-domain, lexicon-based event extraction system that captures various types of modality. |
Sander Bijl de Vroe; Liane Guillou; Miloš Stanojević; Nick McKenna; Mark Steedman; | arxiv-cs.CL | 2021-09-20 |
| 314 | Complex Temporal Question Answering on Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions. |
Zhen Jia; Soumajit Pramanik; Rishiraj Saha Roy; Gerhard Weikum; | arxiv-cs.IR | 2021-09-18 |
| 315 | Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe approaches for developing Emily, an emotion-affective open-domain chatbot. |
WEIXUAN WANG et. al. | arxiv-cs.CL | 2021-09-18 |
| 316 | Benchmarking The Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present EFO-1-QA, a new dataset to benchmark the combinatorial generalizability of CQA models by including 301 different queries types, which is 20 times larger than existing datasets. Besides, our work, for the first time, provides a benchmark to evaluate and analyze the impact of different operators and normal forms by using (a) 7 choices of the operator systems and (b) 9 forms of complex queries. |
Zihao Wang; Hang Yin; Yangqiu Song; | arxiv-cs.CL | 2021-09-18 |
| 317 | RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present RnG-KBQA, a Rank-and-Generate approach for KBQA, which remedies the coverage issue with a generation model while preserving a strong generalization capability. |
Xi Ye; Semih Yavuz; Kazuma Hashimoto; Yingbo Zhou; Caiming Xiong; | arxiv-cs.CL | 2021-09-17 |
| 318 | CodeQA: A Question Answering Dataset for Source Code Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose CodeQA, a free-form question answering dataset for the purpose of source code comprehension: given a code snippet and a question, a textual answer is required to be generated. |
Chenxiao Liu; Xiaojun Wan; | arxiv-cs.CL | 2021-09-17 |
| 319 | Deep Algorithmic Question Answering: Towards A Compositionally Hybrid AI for Algorithmic Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an approach to algorithm reasoning for QA, Deep Algorithmic Question Answering (DAQA), based on three desirable properties: interpretability, generalizability, and robustness which such an AI system should possess, and conclude that they are best achieved with a combination of hybrid and compositional AI. |
Kwabena Nuamah; | arxiv-cs.AI | 2021-09-16 |
| 320 | Knowledge-based Embodied Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Knowledge-based Embodied Question Answering (K-EQA) task, in which the agent intelligently explores the environment to answer various questions with the knowledge. |
Sinan Tan; Mengmeng Ge; Di Guo; Huaping Liu; Fuchun Sun; | arxiv-cs.RO | 2021-09-16 |
| 321 | Topic Transferable Table Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we simulate the practical topic shift scenario by designing novel challenge benchmarks WikiSQL-TS and WikiTQ-TS, consisting of train-dev-test splits in five distinct topic groups, based on the popular WikiSQL and WikiTableQuestions datasets. |
SANEEM AHMED CHEMMENGATH et. al. | arxiv-cs.CL | 2021-09-15 |
| 322 | Transformer-based Language Models for Factoid Question Answering at BioASQ9b Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we describe our experiments and participating systems in the BioASQ Task 9b Phase B challenge of biomedical question answering. |
Urvashi Khanna; Diego Mollá; | arxiv-cs.CL | 2021-09-15 |
| 323 | Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given that pretrained language models have been shown to include world knowledge, we propose to use a unimodal (text-only) train and inference procedure based on automatic off-the-shelf captioning of images and pretrained language models. |
Ander Salaberria; Gorka Azkune; Oier Lopez de Lacalle; Aitor Soroa; Eneko Agirre; | arxiv-cs.CV | 2021-09-15 |
| 324 | Optimising Rolling Stock Planning Including Maintenance with Constraint Programming and Quantum Annealing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We developed and compared Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock optimisation considering necessary maintenance tasks. |
Cristian Grozea; Ronny Hans; Matthias Koch; Christina Riehn; Armin Wolf; | arxiv-cs.AI | 2021-09-15 |
| 325 | Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Conducting experiments on multiple question-answering (QA) datasets, we answer that question negatively: the EP test results do not change significantly when the fine-tuned QA model performs well or in adversarial situations where the model is forced to learn wrong correlations. |
Sagnik Ray Choudhury; Nikita Bhutani; Isabelle Augenstein; | arxiv-cs.CL | 2021-09-15 |
| 326 | End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we extend the boundaries of E2E learning for KGQA to include the training of an ER component. |
Armin Oliya; Amir Saffari; Priyanka Sen; Tom Ayoola; | arxiv-cs.CL | 2021-09-13 |
| 327 | Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a model that explicitly handles multiple-entity questions by implementing a new intersection operation, which identifies the shared elements between two sets of entities. |
Priyanka Sen; Amir Saffari; Armin Oliya; | arxiv-cs.CL | 2021-09-13 |
| 328 | Question Answering Over Electronic Devices: A New Benchmark Dataset and A Multi-Task Learning Based QA Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we meticulously create a large amount of data connected with E-manuals and develop suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. |
ABHILASH NANDY et. al. | arxiv-cs.CL | 2021-09-13 |
| 329 | XGQA: Cross-Lingual Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task. |
JONAS PFEIFFER et. al. | arxiv-cs.CL | 2021-09-13 |
| 330 | SituatedQA: Incorporating Extra-Linguistic Contexts Into QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To study this challenge, we introduce SituatedQA, an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context. |
Michael J. Q. Zhang; Eunsol Choi; | arxiv-cs.CL | 2021-09-13 |
| 331 | Semantic Categorization of Social Knowledge for Commonsense Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we proposed to categorize the semantics needed for these tasks using the SocialIQA as an example. |
Gengyu Wang; Xiaochen Hou; Diyi Yang; Kathleen McKeown; Jing Huang; | arxiv-cs.CL | 2021-09-10 |
| 332 | Counterfactual Adversarial Learning with Representation Interpolation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce Counterfactual Adversarial Training framework (CAT) to tackle the problem from a causality perspective. |
WEI WANG et. al. | arxiv-cs.LG | 2021-09-10 |
| 333 | Temporal Pyramid Transformer with Multimodal Interaction for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Targeting these issues, this paper proposes a novel Temporal Pyramid Transformer (TPT) model with multimodal interaction for VideoQA. |
Min Peng; Chongyang Wang; Yuan Gao; Yu Shi; Xiang-Dong Zhou; | arxiv-cs.CV | 2021-09-10 |
| 334 | Entity-Based Knowledge Conflicts in Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To understand how models use these sources together, we formalize the problem of knowledge conflicts, where the contextual information contradicts the learned information. |
SHAYNE LONGPRE et. al. | arxiv-cs.CL | 2021-09-10 |
| 335 | An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA. |
ZHENGYUAN YANG et. al. | arxiv-cs.CV | 2021-09-10 |
| 336 | TxT: Crossmodal End-to-End Learning with Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address both shortcomings with TxT, a transformer-based crossmodal pipeline that enables fine-tuning both language and visual components on the downstream task in a fully end-to-end manner. |
Jan-Martin O. Steitz; Jonas Pfeiffer; Iryna Gurevych; Stefan Roth; | arxiv-cs.CV | 2021-09-09 |
| 337 | Zero-Shot Dialogue State Tracking Via Cross-Task Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to transfer the \textit{cross-task} knowledge from general question answering (QA) corpora for the zero-shot DST task. |
ZHAOJIANG LIN et. al. | arxiv-cs.CL | 2021-09-09 |
| 338 | Towards Developing A Multilingual and Code-Mixed Visual Question Answering System By Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate these challenges, we propose a knowledge distillation approach to extend an English language-vision model (teacher) into an equally effective multilingual and code-mixed model (student). We also create the large-scale multilingual and code-mixed VQA dataset in eleven different language setups considering the multiple Indian and European languages. |
Humair Raj Khan; Deepak Gupta; Asif Ekbal; | arxiv-cs.CL | 2021-09-09 |
| 339 | From Philosophy to Interfaces: An Explanatory Method and A Tool Inspired By Achinstein’s Theory of Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new method for explanations in Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. |
Francesco Sovrano; Fabio Vitali; | arxiv-cs.AI | 2021-09-09 |
| 340 | Formal Query Building with Query Structure Prediction for Complex Question Answering Over Knowledge Base IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new formal query building approach that consists of two stages. |
Yongrui Chen; Huiying Li; Yuncheng Hua; Guilin Qi; | arxiv-cs.CL | 2021-09-08 |
| 341 | R2-D2: A Modular Baseline for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). |
Martin Fajcik; Martin Docekal; Karel Ondrej; Pavel Smrz; | arxiv-cs.CL | 2021-09-08 |
| 342 | ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering Over Archival News Collections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to foster the research in the field of ODQA on such historical collections, we present ArchivalQA, a large question answering dataset consisting of 1,067,056 question-answer pairs which is designed for temporal news QA. |
Jiexin Wang; Adam Jatowt; Masatoshi Yoshikawa; | arxiv-cs.CL | 2021-09-08 |
| 343 | It Is AI’s Turn to Ask Human A Question: Question and Answer Pair Generation for Children Storybooks in FairytaleQA Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With a newly released book QA dataset (FairytaleQA), which educational experts labeled on 46 fairytale storybooks for early childhood readers, we developed an automated QA generation model architecture for this novel application. |
BINGSHENG YAO et. al. | arxiv-cs.CL | 2021-09-08 |
| 344 | Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage. |
Chenyu You; Nuo Chen; Yuexian Zou; | arxiv-cs.CL | 2021-09-07 |
| 345 | Exploiting Reasoning Chains for Multi-hop Science Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering. |
Weiwen Xu; Yang Deng; Huihui Zhang; Deng Cai; Wai Lam; | arxiv-cs.CL | 2021-09-07 |
| 346 | Improving Numerical Reasoning Skills in The Modular Approach for Complex Question Answering on Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose effective techniques to improve NMNs’ numerical reasoning capabilities by making the interpreter question-aware and capturing the relationship between entities and numbers. |
Xiao-Yu Guo; Yuan-Fang Li; Gholamreza Haffari; | arxiv-cs.CL | 2021-09-06 |
| 347 | Improved RAMEN: Towards Domain Generalization for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study provides two major improvements to the early/late fusion module and aggregation module of the RAMEN architecture, with the objective of further strengthening domain generalization. |
Bhanuka Manesha Samarasekara Vitharana Gamage; Lim Chern Hong; | arxiv-cs.CV | 2021-09-06 |
| 348 | General-Purpose Question-Answering with Macaw Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response, we present Macaw, a versatile, generative question-answering (QA) system that we are making available to the community. |
Oyvind Tafjord; Peter Clark; | arxiv-cs.CL | 2021-09-06 |
| 349 | Transformer Models for Text Coherence Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, in this paper, we propose four different Transformer-based architectures for the task: vanilla Transformer, hierarchical Transformer, multi-task learning-based model, and a model with fact-based input representation. |
Tushar Abhishek; Daksh Rawat; Manish Gupta; Vasudeva Varma; | arxiv-cs.CL | 2021-09-05 |
| 350 | Contextualized Embeddings Based Convolutional Neural Networks for Duplicate Question Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel architecture combining a Bidirectional Transformer Encoder with Convolutional Neural Networks for the QPI task. |
Harsh Sakhrani; Saloni Parekh; Pratik Ratadiya; | arxiv-cs.CL | 2021-09-03 |
| 351 | Challenges in Generalization in Open Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Drawing upon studies on systematic generalization, we introduce and annotate questions according to three categories that measure different levels and kinds of generalization: training set overlap, compositional generalization (comp-gen), and novel-entity generalization (novel-entity). |
Linqing Liu; Patrick Lewis; Sebastian Riedel; Pontus Stenetorp; | arxiv-cs.CL | 2021-09-02 |
| 352 | WebQA: Multihop and Multimodal QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to bridge this gap between the natural language and computer vision communities with WebQA. |
YINGSHAN CHANG et. al. | arxiv-cs.CL | 2021-09-01 |
| 353 | Does Knowledge Help General NLU? An Empirical Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we empirically investigated the contribution of external knowledge by measuring the end-to-end performance of language models with various knowledge integration methods. |
Ruochen Xu; Yuwei Fang; Chenguang Zhu; Michael Zeng; | arxiv-cs.CL | 2021-09-01 |
| 354 | Structured Context and High-Coverage Grammar for Conversational Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new Logical Form (LF) grammar that can model a wide range of queries on the graph while remaining sufficiently simple to generate supervision data efficiently. |
Pierre Marion; Paweł Krzysztof Nowak; Francesco Piccinno; | arxiv-cs.CL | 2021-09-01 |
| 355 | Unsupervised Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our exploration in this work inspiringly shows unsupervised ODQA can reach up to 86% performance of supervised ones. |
Pengfei Zhu; Xiaoguang Li; Jian Li; Hai Zhao; | arxiv-cs.CL | 2021-08-31 |
| 356 | When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. |
Zixian Huang; Ao Wu; Yulin Shen; Gong Cheng; Yuzhong Qu; | arxiv-cs.CL | 2021-08-31 |
| 357 | Generating Answer Candidates for Quizzes and Answer-Aware Question Generators Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators. |
KRISTIYAN VACHEV et. al. | arxiv-cs.CL | 2021-08-29 |
| 358 | On The Significance of Question Encoder Sequence Model in The Out-of-Distribution Performance in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To demonstrate this, we performed a detailed analysis of various existing RNN-based and Transformer-based question-encoders, and along, we proposed a novel Graph attention network (GAT)-based question-encoder. |
Gouthaman KV; Anurag Mittal; | arxiv-cs.CV | 2021-08-28 |
| 359 | QACE: Asking Questions to Evaluate An Image Caption Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose QACE, a new metric based on Question Answering for Caption Evaluation. |
Hwanhee Lee; Thomas Scialom; Seunghyun Yoon; Franck Dernoncourt; Kyomin Jung; | arxiv-cs.CL | 2021-08-27 |
| 360 | Query-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents Macquarie University’s participation to the BioASQ Synergy Task, and BioASQ9b Phase B. |
Diego Mollá; Urvashi Khanna; Dima Galat; Vincent Nguyen; Maciej Rybinski; | arxiv-cs.CL | 2021-08-27 |
| 361 | Semantic-Based Self-Critical Training For Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The results obtained showed that use a semantic-based REINFORCE algorithm for the question generation syntactically reshapes the generated questions while preserving their underlying semantic meaning. |
Loïc; Kwate Dassi; | arxiv-cs.CL | 2021-08-26 |
| 362 | Sequence-to-Sequence Learning on Keywords for Efficient FAQ Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes TI-S2S, a novel learning framework combining TF-IDF based keyword extraction and Word2Vec embeddings for training a Sequence-to-Sequence (Seq2Seq) architecture. |
Sourav Dutta; Haytham Assem; Edward Burgin; | arxiv-cs.IR | 2021-08-23 |
| 363 | External Knowledge Enabled Text Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the generalized use of external knowledge to augment our understanding of the said scene-text. |
Arka Ujjal Dey; Ernest Valveny; Gaurav Harit; | arxiv-cs.CV | 2021-08-22 |
| 364 | UzBERT: Pretraining A BERT Model for Uzbek Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce UzBERT, a pretrained Uzbek language model based on the BERT architecture. |
B. Mansurov; A. Mansurov; | arxiv-cs.CL | 2021-08-22 |
| 365 | UNIQORN: Unified Question Answering Over RDF Knowledge Graphs and Natural Language Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the first QA system that can seamlessly operate over RDF datasets and text corpora, or both together, in a unified framework. |
Soumajit Pramanik; Jesujoba Alabi; Rishiraj Saha Roy; Gerhard Weikum; | arxiv-cs.IR | 2021-08-19 |
| 366 | Localize, Group, and Select: Boosting Text-VQA By Scene Text Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Localize, Group, and Select (LOGOS), a novel model which attempts to tackle this problem from multiple aspects. |
Xiaopeng Lu; Zhen Fan; Yansen Wang; Jean Oh; Carolyn P. Rose; | arxiv-cs.CV | 2021-08-19 |
| 367 | SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantic knowledge from neighbors of linked-entity. |
TAOLIN ZHANG et. al. | arxiv-cs.CL | 2021-08-19 |
| 368 | Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering Over Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents CLOCQ, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. |
Philipp Christmann; Rishiraj Saha Roy; Gerhard Weikum; | arxiv-cs.IR | 2021-08-19 |
| 369 | VerSaChI: Finding Statistically Significant Subgraph Matches Using Chebyshev’s Inequality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose VerSaChI for finding the top-k most similar subgraphs based on 2-hop label and structural overlap similarity with the query. |
Shubhangi Agarwal; Sourav Dutta; Arnab Bhattacharya; | arxiv-cs.DB | 2021-08-18 |
| 370 | The Multi-Modal Video Reasoning and Analyzing Competition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021. |
HAORAN PENG et. al. | arxiv-cs.CV | 2021-08-18 |
| 371 | MeDiaQA: A Question Answering Dataset on Medical Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce MeDiaQA, a novel question answering(QA) dataset, which constructed on real online Medical Dialogues. |
Huqun Suri; Qi Zhang; Wenhua Huo; Yan Liu; Chunsheng Guan; | arxiv-cs.CL | 2021-08-18 |
| 372 | Generative Relation Linking for Question Answering Over Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel approach for relation linking framing it as a generative problem facilitating the use of pre-trained sequence-to-sequence models. |
GAETANO ROSSIELLO et. al. | arxiv-cs.CL | 2021-08-16 |
| 373 | Complex Knowledge Base Question Answering: A Survey Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this survey, we review recent advances on KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. |
YUNSHI LAN et. al. | arxiv-cs.CL | 2021-08-15 |
| 374 | HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents HiTab, a free and open dataset to study question answering (QA) and natural language generation (NLG) over hierarchical tables. |
ZHOUJUN CHENG et. al. | arxiv-cs.CL | 2021-08-15 |
| 375 | Semantic Answer Similarity for Evaluating Question Answering Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this short paper, we present SAS, a cross-encoder-based metric for the estimation of semantic answer similarity, and compare it to seven existing metrics. To this end, we create an English and a German three-way annotated evaluation dataset containing pairs of answers along with human judgment of their semantic similarity, which we release along with an implementation of the SAS metric and the experiments. |
Julian Risch; Timo Möller; Julian Gutsch; Malte Pietsch; | arxiv-cs.CL | 2021-08-13 |
| 376 | MRD-Net: Multi-Modal Residual Knowledge Distillation for Spoken Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, this work proposes a novel multi-modal residual knowledge distillation method (MRD-Net), which further distills knowledge at the acoustic level from the audio-assistant (Audio-A). |
Chenyu You; Nuo Chen; Yuexian Zou; | ijcai | 2021-08-13 |
| 377 | A Dataset for Answering Time-Sensitive Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to promote research in this direction, we propose to construct a time-sensitive QA dataset. |
Wenhu Chen; Xinyi Wang; William Yang Wang; | arxiv-cs.CL | 2021-08-13 |
| 378 | AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this, in this work, we attempt to tackle the language prior problem from the viewpoint of the feature space learning. |
YANGYANG GUO et. al. | ijcai | 2021-08-13 |
| 379 | Zero-shot Task Transfer for Invoice Extraction Via Class-aware QA Ensemble Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present VESPA, an intentionally simple yet novel zero-shot system for layout, locale, and domain agnostic document extraction. |
Prithiviraj Damodaran; Prabhkaran Singh; Josemon Achankuju; | arxiv-cs.IR | 2021-08-13 |
| 380 | Automatically Paraphrasing Via Sentence Reconstruction and Round-trip Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework for paraphrase generation. |
ZILU GUO et. al. | ijcai | 2021-08-13 |
| 381 | A Compression-Compilation Framework for On-mobile Real-time BERT Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model meets both resource and real-time specifications of mobile devices. |
WEI NIU et. al. | ijcai | 2021-08-13 |
| 382 | A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we elaborately summarize the typical challenges and solutions for complex KBQA. |
YUNSHI LAN et. al. | ijcai | 2021-08-13 |
| 383 | Chop Chop BERT: Visual Question Answering By Chopping VisualBERT’s Heads Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling 12 different types of questions. |
Chenyu Gao; Qi Zhu; Peng Wang; Qi Wu; | ijcai | 2021-08-13 |
| 384 | Controllable Generation from Pre-trained Language Models Via Inverse Prompting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The core idea of inverse prompting is to use generated text to inversely predict the prompt during beam search, which enhances the relevance between the prompt and the generated text and thus improves controllability. |
XU ZOU et. al. | kdd | 2021-08-12 |
| 385 | Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we identify and address two underlying problems of dense retrievers: i)~fragility to training data noise and ii)~requiring large batches to robustly learn the embedding space. |
Luyu Gao; Jamie Callan; | arxiv-cs.IR | 2021-08-12 |
| 386 | How Optimal Is Greedy Decoding for Extractive Question Answering? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To study the performance and optimality of greedy decoding, we present exact-extract, a decoding algorithm that efficiently finds the most probable answer span in the context. |
Or Castel; Ori Ram; Avia Efrat; Omer Levy; | arxiv-cs.CL | 2021-08-12 |
| 387 | Enhancing Taxonomy Completion with Concept Generation Via Fusing Relational Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve the limitations of extraction-based methods, we propose GenTaxo to enhance taxonomy completion by identifying positions in existing taxonomies that need new concepts and then generating appropriate concept names. |
Qingkai Zeng; Jinfeng Lin; Wenhao Yu; Jane Cleland-Huang; Meng Jiang; | kdd | 2021-08-12 |
| 388 | Neural-Answering Logical Queries on Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an embedding based method (NewLook) to address these limitations. |
Lihui Liu; Boxin Du; Heng Ji; ChengXiang Zhai; Hanghang Tong; | kdd | 2021-08-12 |
| 389 | FedMatch: Federated Learning Over Heterogeneous Question Answering Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to adopt federated learning for QA with the special concern on the statistical heterogeneity of the QA data. |
Jiangui Chen; Ruqing Zhang; Jiafeng Guo; Yixing Fan; Xueqi Cheng; | arxiv-cs.IR | 2021-08-11 |
| 390 | Mounting Video Metadata on Transformer-based Language Model for Open-ended Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we challenge the existing multiple-choice video question answering by changing it to open-ended video question answering. |
Donggeon Lee; Seongho Choi; Youwon Jang; Byoung-Tak Zhang; | arxiv-cs.CV | 2021-08-11 |
| 391 | Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an approach for retrieving both texts and tables relevant to a question by jointly encoding texts, tables and questions into a single vector space. To this end, we create a new multi-modal dataset based on text and table datasets from related work and compare the retrieval performance of different encoding schemata. We release the newly created multi-modal dataset to the community so that it can be used for training and evaluation. |
Bogdan Kostić; Julian Risch; Timo Möller; | arxiv-cs.CL | 2021-08-09 |
| 392 | Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a hybrid framework that takes both textual and tabular evidence as input and generates either direct answers or SQL queries depending on which form could better answer the question. |
ALEXANDER HANBO LI et. al. | arxiv-cs.CL | 2021-08-05 |
| 393 | Decoupled Transformer for Scalable Inference in Open-domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To reduce computational cost and latency, we propose decoupling the transformer MRC model into input-component and cross-component. |
Haytham ElFadeel; Stan Peshterliev; | arxiv-cs.CL | 2021-08-05 |
| 394 | Question-controlled Text-aware Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel Geometry and Question Aware Model (GQAM). Based on two existing text-aware captioning datasets, we automatically construct two datasets, ControlTextCaps and ControlVizWiz to support the task. |
Anwen Hu; Shizhe Chen; Qin Jin; | arxiv-cs.CV | 2021-08-04 |
| 395 | Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management, one of the most challenging forms of clinical decision-making. |
CÉCILE LOGÉ et. al. | arxiv-cs.CL | 2021-08-03 |
| 396 | Extending Sticky-Datalog+/- Via Finite-Position Selection Functions: Tractability, Algorithms, and Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a bottom-up QA algorithm for programs in the class sch(S), for a general selection function S. |
Leopoldo Bertossi; Mostafa Milani; | arxiv-cs.DB | 2021-08-02 |
| 397 | MuSiQue: Multi-hop Questions Via Single-hop Question Composition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a bottom-up approach that systematically selects composable pairs of single-hop questions that are connected, i.e., where one reasoning step requires information from the other. Can we create a question answering (QA) dataset that, by construction, requires proper multi-hop reasoning? We also build a more challenging dataset, MuSiQue-Full, consisting of answerable and unanswerable contrast question pairs, where model performance drops further by 14 F1 pts. |
Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal; | arxiv-cs.CL | 2021-08-01 |
| 398 | Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To handle more complex questions, we aim at creating knowledge base from clinical notes to link different patients and clinical notes, and performing knowledge base question answering (KBQA). Based on the expert annotations in n2c2, we first created the ClinicalKBQA dataset that includes 8,952 QA pairs and covers questions about seven medical topics through 322 question templates. |
Ping Wang; Tian Shi; Khushbu Agarwal; Sutanay Choudhury; Chandan K. Reddy; | arxiv-cs.CL | 2021-08-01 |
| 399 | Automatic Claim Review for Climate Science Via Explanation Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we try to aid them by automating generating explanation for a predicted veracity label for a claim by deploying the approach used in open domain question answering of a fusion in decoder augmented with retrieved supporting passages from an external knowledge. |
Shraey Bhatia; Jey Han Lau; Timothy Baldwin; | arxiv-cs.CL | 2021-07-30 |
| 400 | Talk2Data: High-Level Question Decomposition for Data-Oriented Question and Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issue, in this paper, we introduce Talk2Data, a data-oriented online question and answering system that supports answering both low-level and high-level questions. |
DANQING SHI et. al. | arxiv-cs.HC | 2021-07-29 |
| 401 | An Online Question Answering System Based on Sub-graph Searching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this pa-per, we design a sub-graph searching mechanism to solve this problem by creating sub-graph index, and each answer generation step is restricted in the sub-graph level. |
Shuangyong Song; | arxiv-cs.AI | 2021-07-28 |
| 402 | QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The study is aimed at both practitioners looking for pointers to the wealth of existing data, and at researchers working on new resources. |
Anna Rogers; Matt Gardner; Isabelle Augenstein; | arxiv-cs.CL | 2021-07-27 |
| 403 | In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new method to enhance the reasoning capabilities of a multi-modal pretrained model (Vision+Language BERT) by integrating facts extracted from an external knowledge base. |
Peter Vickers; Nikolaos Aletras; Emilio Monti; Lo?c Barrault; | acl | 2021-07-26 |
| 404 | Towards More Equitable Question Answering Systems: How Much More Data Do You Need? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this project we take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. |
Arnab Debnath; Navid Rajabi; Fardina Fathmiul Alam; Antonios Anastasopoulos; | acl | 2021-07-26 |
| 405 | Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework, ExCorD (Explicit guidance on how to resolve Conversational Dependency) to enhance the abilities of QA models in comprehending conversational context. |
Gangwoo Kim; Hyunjae Kim; Jungsoo Park; Jaewoo Kang; | acl | 2021-07-26 |
| 406 | Joint Models for Answer Verification in Question Answering Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. |
Zeyu Zhang; Thuy Vu; Alessandro Moschitti; | acl | 2021-07-26 |
| 407 | Multi-Scale Progressive Attention Network for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a novel Multi-Scale Progressive Attention Network (MSPAN) to achieve relational reasoning between cross-scale video information. |
Zhicheng Guo; Jiaxuan Zhao; Licheng Jiao; Xu Liu; Lingling Li; | acl | 2021-07-26 |
| 408 | StructuralLM: Structural Pre-training for Form Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents. |
CHENLIANG LI et. al. | acl | 2021-07-26 |
| 409 | Improving Factual Consistency of Abstractive Summarization Via Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present an approach to address factual consistency in summarization. |
FENG NAN et. al. | acl | 2021-07-26 |
| 410 | Multi-Task Retrieval for Knowledge-Intensive Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Driven by the question of whether a neural retrieval model can be _universal_ and perform robustly on a wide variety of problems, we propose a multi-task trained model. |
JEAN MAILLARD et. al. | acl | 2021-07-26 |
| 411 | On The Efficacy of Adversarial Data Collection for Question Answering: Results from A Large-Scale Randomized Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). |
Divyansh Kaushik; Douwe Kiela; Zachary C. Lipton; Wen-tau Yih; | acl | 2021-07-26 |
| 412 | A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. |
TAHIRA NASEEM et. al. | acl | 2021-07-26 |
| 413 | Check It Again:Progressive Visual Question Answering Via Visual Entailment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a select-and-rerank (SAR) progressive framework based on Visual Entailment. |
Qingyi Si; Zheng Lin; Ming yu Zheng; Peng Fu; Weiping Wang; | acl | 2021-07-26 |
| 414 | Question Answering Over Temporal Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. |
Apoorv Saxena; Soumen Chakrabarti; Partha Talukdar; | acl | 2021-07-26 |
| 415 | One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Cross-lingual Open-Retrieval Answer Generation (CORA), the first unified many-to-many question answering (QA) model that can answer questions across many languages, even for ones without language-specific annotated data or knowledge sources. |
Akari Asai; Xinyan Yu; Jungo Kasai; Hannaneh Hajishirzi; | arxiv-cs.CL | 2021-07-26 |
| 416 | XMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new contrastive learning method called Cross Momentum Contrastive learning (xMoCo), for learning a dual-encoder model for question-passage matching. |
Nan Yang; Furu Wei; Binxing Jiao; Daxing Jiang; Linjun Yang; | acl | 2021-07-26 |
| 417 | ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models Via Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text. |
YUJIA QIN et. al. | acl | 2021-07-26 |
| 418 | Few-Shot Question Answering By Pretraining Span Selection IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new pretraining scheme tailored for question answering: recurring span selection. |
Ori Ram; Yuval Kirstain; Jonathan Berant; Amir Globerson; Omer Levy; | acl | 2021-07-26 |
| 419 | A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, we propose a novel Multi-Task Learning (MTL) method with data augmentation for medical question understanding. |
KHALIL MRINI et. al. | acl | 2021-07-26 |
| 420 | TAT-QA: A Question Answering Benchmark on A Hybrid of Tabular and Textual Content in Finance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions. |
FENGBIN ZHU et. al. | acl | 2021-07-26 |
| 421 | Guiding The Growth: Difficulty-Controllable Question Generation Through Step-by-Step Rewriting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. |
YI CHENG et. al. | acl | 2021-07-26 |
| 422 | Towards Visual Question Answering on Pathology Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to develop a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images. |
XUEHAI HE et. al. | acl | 2021-07-26 |
| 423 | A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an architecture for joint document and snippet ranking, the two middle stages, which leverages the intuition that relevant documents have good snippets and good snippets come from relevant documents. |
Dimitris Pappas; Ion Androutsopoulos; | acl | 2021-07-26 |
| 424 | Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. |
Yunshi Lan; Jing Jiang; | acl | 2021-07-26 |
| 425 | Coreference Reasoning in Machine Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set. |
Mingzhu Wu; Nafise Sadat Moosavi; Dan Roth; Iryna Gurevych; | acl | 2021-07-26 |
| 426 | A Mutual Information Maximization Approach for The Spurious Solution Problem in Weakly Supervised Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to alleviate the spurious solution problem, we propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions. |
Zhihong Shao; Lifeng Shang; Qun Liu; Minlie Huang; | acl | 2021-07-26 |
| 427 | End-to-End Training of Neural Retrievers for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we systematically study retriever pre-training. |
DEVENDRA SACHAN et. al. | acl | 2021-07-26 |
| 428 | UnitedQA: A Hybrid Approach for Open Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid approach for leveraging the strengths of both models. |
HAO CHENG et. al. | acl | 2021-07-26 |
| 429 | Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Adaptive Passage Encoder, an AC method that can be applied to an existing ODQA model and can be trained efficiently on a single GPU. |
Yuxiang Wu; Pasquale Minervini; Pontus Stenetorp; Sebastian Riedel; | acl | 2021-07-26 |
| 430 | Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We construct a new dataset of closed-book QA using SQuAD, and investigate the performance of BART. |
Cunxiang Wang; Pai Liu; Yue Zhang; | acl | 2021-07-26 |
| 431 | CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions. |
Zhengbao Jiang; Jialong Han; Bunyamin Sisman; Xin Luna Dong; | acl | 2021-07-26 |
| 432 | Learning to Perturb Word Embeddings for Out-of-distribution QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a simple yet effective DA method based on a stochastic noise generator, which learns to perturb the word embedding of the input questions and context without changing their semantics. |
Seanie Lee; Minki Kang; Juho Lee; Sung Ju Hwang; | acl | 2021-07-26 |
| 433 | Consistency Regularization for Cross-Lingual Fine-Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to improve cross-lingual fine-tuning with consistency regularization. |
BO ZHENG et. al. | acl | 2021-07-26 |
| 434 | Pre-training Universal Language Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. |
Yian Li; Hai Zhao; | acl | 2021-07-26 |
| 435 | Efficient Passage Retrieval with Hashing for Open-domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever (DPR) to represent the passage index using compact binary codes rather than continuous vectors. |
Ikuya Yamada; Akari Asai; Hannaneh Hajishirzi; | acl | 2021-07-26 |
| 436 | Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address these problems by a novel Rationale-Enriched Answer Generator (REAG), which incorporates an extractive mechanism into a generative model. |
Chenliang Li; Bin Bi; Ming Yan; Wei Wang; Songfang Huang; | acl | 2021-07-26 |
| 437 | Mind Your Outliers! Investigating The Negative Impact of Outliers on Active Learning for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers – groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). |
Siddharth Karamcheti; Ranjay Krishna; Li Fei-Fei; Christopher Manning; | acl | 2021-07-26 |
| 438 | Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our controlled experiments suggest two headrooms – paragraph selection and answerability prediction, i.e. whether the paired evidence document contains the answer to the query or not. |
Akari Asai; Eunsol Choi; | acl | 2021-07-26 |
| 439 | Towards A More Robust Evaluation for Conversational Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we highlight this effect and propose new tools for evaluation and training in order to guard against the noted issues. |
Wissam Siblini; Baris Sayil; Yacine Kessaci; | acl | 2021-07-26 |
| 440 | Learning Dense Representations of Phrases at Scale IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an effective method to learn phrase representations from the supervision of reading comprehension tasks, coupled with novel negative sampling methods. |
Jinhyuk Lee; Mujeen Sung; Jaewoo Kang; Danqi Chen; | acl | 2021-07-26 |
| 441 | Learning Faithful Representations of Causal Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define the faithfulness property of contextual embeddings to capture geometric distance-based properties of directed acyclic causal graphs. |
Ananth Balashankar; Lakshminarayanan Subramanian; | acl | 2021-07-26 |
| 442 | Answering Ambiguous Questions Through Generative Evidence Fusion and Round-Trip Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions. |
YIFAN GAO et. al. | acl | 2021-07-26 |
| 443 | Zero-shot Event Extraction Via Transfer Learning: Challenges and Insights Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. A city was attacked entails There is an attack), exploiting pretrained TE/QA models for direct transfer. |
Qing Lyu; Hongming Zhang; Elior Sulem; Dan Roth; | acl | 2021-07-26 |
| 444 | Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a supervised data mining method using an accurate early fusion model to improve the training of an efficient late fusion retrieval model. |
Yinfei Yang; Ning Jin; Kuo Lin; Mandy Guo; Daniel Cer; | acl | 2021-07-26 |
| 445 | Continuous Language Generative Flow Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a flow-based language generation model by adapting previous flow generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation. |
Zineng Tang; Shiyue Zhang; Hyounghun Kim; Mohit Bansal; | acl | 2021-07-26 |
| 446 | Learning Event Graph Knowledge for Abductive Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, we propose a variational autoencoder based model ege-RoBERTa, which employs a latent variable to capture the necessary commonsense knowledge from event graph for guiding the abductive reasoning task. |
Li Du; Xiao Ding; Ting Liu; Bing Qin; | acl | 2021-07-26 |
| 447 | CoSQA: 20,000+ Web Queries for Code Search and Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy this, we introduce CoSQA dataset. It includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators. |
JUNJIE HUANG et. al. | acl | 2021-07-26 |
| 448 | Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Motion-Appearance Synergistic Networks (MASN), which embed two cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question’s intentions. |
Ahjeong Seo; Gi-Cheon Kang; Joonhan Park; Byoung-Tak Zhang; | acl | 2021-07-26 |
| 449 | Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization. |
Shweta Yadav; Deepak Gupta; Asma Ben Abacha; Dina Demner-Fushman; | acl | 2021-07-26 |
| 450 | QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on the zero-shot slot-filling problem, where the model needs to predict slots and their values, given utterances from new domains without training on the target domain. |
XINYA DU et. al. | acl | 2021-07-26 |
| 451 | GTM: A Generative Triple-wise Model for Conversational Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these problems, we propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG). |
Lei Shen; Fandong Meng; Jinchao Zhang; Yang Feng; Jie Zhou; | acl | 2021-07-26 |
| 452 | A Semantic-based Method for Unsupervised Commonsense Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering. |
YILIN NIU et. al. | acl | 2021-07-26 |
| 453 | Robustifying Multi-hop QA Through Pseudo-Evidentiality Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast, we propose a new approach to learn evidentiality, deciding whether the answer prediction is supported by correct evidences, without such annotations. |
Kyungjae Lee; Seung-won Hwang; Sang-eun Han; Dohyeon Lee; | acl | 2021-07-26 |
| 454 | Explanations for CommonsenseQA: New Dataset and Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our explanation definition is based on certain desiderata, and translates an explanation into a set of positive and negative common-sense properties (aka facts) which not only explain the correct answer choice but also refute the incorrect ones. |
SHOURYA AGGARWAL et. al. | acl | 2021-07-26 |
| 455 | ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To showcase the usefulness of this task formulation, we introduce ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. |
WOOJEONG JIN et. al. | acl | 2021-07-26 |
| 456 | X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate OOD generalization in VQA as a compositional generalization problem and propose a graph generative modeling-based training scheme (X-GGM) to implicitly model the problem. |
Jingjing Jiang; Ziyi Liu; Yifan Liu; Zhixiong Nan; Nanning Zheng; | arxiv-cs.CV | 2021-07-24 |
| 457 | The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the use of bilingual intermediate pretraining as a reliable technique to derive large and consistent performance gains on three different NLP tasks using code-switched text. |
Archiki Prasad; Mohammad Ali Rehan; Shreya Pathak; Preethi Jyothi; | arxiv-cs.CL | 2021-07-21 |
| 458 | CogME: A Novel Evaluation Metric for Video Understanding Intelligence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To examine the suitability of a VideoQA dataset for validating video understanding intelligence, we evaluated the baseline model of the DramaQA dataset by applying CogME. |
MINJUNG SHIN et. al. | arxiv-cs.CV | 2021-07-20 |
| 459 | Separating Skills and Concepts for Novel Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel method for learning to compose skills and concepts that separates these two factors implicitly within a model by learning grounded concept representations and disentangling the encoding of skills from that of concepts. |
Spencer Whitehead; Hui Wu; Heng Ji; Rogerio Feris; Kate Saenko; | arxiv-cs.CV | 2021-07-19 |
| 460 | A Discriminative Semantic Ranker for Question Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to tackle the dilemma of the first-stage ranker, and propose a discriminative semantic ranker, namely DenseTrans, for high-recall retrieval. |
YINQIONG CAI et. al. | arxiv-cs.IR | 2021-07-17 |
| 461 | A Survey of Knowledge Graph Embedding and Their Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces growth in the field of KG embedding from simple translation-based models to enrichment-based models. |
Shivani Choudhary; Tarun Luthra; Ashima Mittal; Rajat Singh; | arxiv-cs.IR | 2021-07-16 |
| 462 | Exploiting Rich Syntax for Better Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees.In this paper, we propose an approach to learn syntax-based representations for KBQA. |
Pengju Zhang; Yonghui Jia; Muhua Zhu; Wenliang Chen; Min Zhang; | arxiv-cs.CL | 2021-07-16 |
| 463 | Spanish Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the Spanish RoBERTa-base and RoBERTa-large models, as well as the corresponding performance evaluations. |
ASIER GUTIÉRREZ-FANDIÑO et. al. | arxiv-cs.CL | 2021-07-15 |
| 464 | Reinforcement Learning from Reformulations In Conversational Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we take a step towards a more natural learning paradigm – from noisy and implicit feedback via question reformulations. |
Magdalena Kaiser; Rishiraj Saha Roy; Gerhard Weikum; | sigir | 2021-07-13 |
| 465 | Multi-Type Textual Reasoning for Product-Aware Answer Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a review-attribute heterogeneous graph neural network (abbreviated as RAHGNN) to model the logical relation of all multi-type text. |
Yue Feng; Zhaochun Ren; Weijie Zhao; Mingming Sun; Ping Li; | sigir | 2021-07-13 |
| 466 | Answering Any-hop Open-domain Questions with Iterative Document Reranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determines when to stop the retrieval process. |
Yuyu Zhang; Ping Nie; Arun Ramamurthy; Le Song; | sigir | 2021-07-13 |
| 467 | Synthetic Target Domain Supervision for Open Retrieval QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we stress-test the Dense Passage Retriever (DPR)—a state-of-the-art (SOTA) open domain neural retrieval model—on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting. |
REVANTH GANGI REDDY et. al. | sigir | 2021-07-13 |
| 468 | LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel Language-Prior Feedback (LPF) objective function, to re-balance the proportion of each answer’s loss value in the total VQA loss. |
Zujie Liang; Haifeng Hu; Jiaying Zhu; | sigir | 2021-07-13 |
| 469 | Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Graphhopper, a novel method that approaches the task by integrating knowledge graph reasoning, computer vision, and natural language processing techniques. |
RAJAT KONER et. al. | arxiv-cs.CV | 2021-07-13 |
| 470 | AliMe DA: A Data Augmentation Framework for Question Answering in Cold-start Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose AliMe DA, a practical data augmentation (DA) framework that consists of data production, denoising and consumption, to alleviate this problem. |
GUOHAI XU et. al. | sigir | 2021-07-13 |
| 471 | Passage Retrieval for Outside-Knowledge Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address multi-modal information needs that contain text questions and images by focusing on passage retrieval for outside-knowledge visual question answering. |
Chen Qu; Hamed Zamani; Liu Yang; W. Bruce Croft; Erik Learned-Miller; | sigir | 2021-07-13 |
| 472 | Event Occurrence Date Estimation Based on Multivariate Time Series Analysis Over Temporal Document Collections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose an approach to temporal profiling of event mentions in text. |
Jiexin Wang; Adam Jatowt; Masatoshi Yoshikawa; | sigir | 2021-07-13 |
| 473 | Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple and effective strategy for data augmentation for low-resource machine reading comprehension (MRC). |
Hoang Van; Vikas Yadav; Mihai Surdeanu; | sigir | 2021-07-13 |
| 474 | Answer Complex Questions: Path Ranker Is All You Need Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a purely rank-based framework Thinking Path Re-Ranker (TPRR), which is comprised of Thinking Path Ranker (TPR) for generating document sequences called "a path" and External Path Reranker (EPR) for selecting the best path from candidate paths generated by TPR. |
XINYU ZHANG et. al. | sigir | 2021-07-13 |
| 475 | Zero-shot Visual Question Answering Using Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge these gaps, in this paper, we propose a Zero-shot VQA algorithm using knowledge graphs and a mask-based learning mechanism for better incorporating external knowledge, and present new answer-based Zero-shot VQA splits for the F-VQA dataset. |
ZHUO CHEN et. al. | arxiv-cs.AI | 2021-07-12 |
| 476 | Deep Neural Networks Evolve Human-like Attention Distribution During Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we investigate whether humans and DNNs allocate attention in comparable ways when reading a text passage to subsequently answer a specific question. |
Jiajie Zou; Nai Ding; | arxiv-cs.CL | 2021-07-12 |
| 477 | PonderNet: Learning to Ponder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this limitation we introduce PonderNet, a new algorithm that learns to adapt the amount of computation based on the complexity of the problem at hand. |
Andrea Banino; Jan Balaguer; Charles Blundell; | arxiv-cs.LG | 2021-07-12 |
| 478 | DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on these observations, we propose a Dual-Visual Graph Reasoning Unit (DualVGR) which reasons over videos in an end-to-end fashion. |
Jianyu Wang; Bing-Kun Bao; Changsheng Xu; | arxiv-cs.MM | 2021-07-10 |
| 479 | Similar Cases Recommendation Using Legal Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this demo, we describe our solution for predicting similar nodes in a case graph derived from our legal knowledge graph. |
Jaspreet Singh Dhani; Ruchika Bhatt; Balaji Ganesan; Parikshet Sirohi; Vasudha Bhatnagar; | arxiv-cs.AI | 2021-07-10 |
| 480 | An Initial Investigation of Non-Native Spoken Question-Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The SQA task considered in this paper is to extract the answer from a candidate$\text{‘}$s spoken response to a question in a prompt-response style language assessment test. |
Vatsal Raina; Mark J. F. Gales; | arxiv-cs.CL | 2021-07-09 |
| 481 | Reasoning Over Virtual Knowledge Bases With Open Predicate Relations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text. |
Haitian Sun; Patrick Verga; Bhuwan Dhingra; Ruslan Salakhutdinov; William W Cohen; | icml | 2021-07-08 |
| 482 | Unifying Vision-and-Language Tasks Via Text Generation IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs. |
Jaemin Cho; Jie Lei; Hao Tan; Mohit Bansal; | icml | 2021-07-08 |
| 483 | Poolingformer: Long Document Modeling with Pooling Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a two-level attention schema, Poolingformer, for long document modeling. |
HANG ZHANG et. al. | icml | 2021-07-08 |
| 484 | LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we present LEGO, a Latent Execution-Guided reasOning framework to handle this challenge in KGQA. |
HONGYU REN et. al. | icml | 2021-07-08 |
| 485 | MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Since most current medical VQA models focus on visual content, ignoring the importance of text, this paper proposes a multi-view attention-based model(MuVAM) for medical visual question answering which integrates the high-level semantics of medical images on the basis of text description. |
HAIWEI PAN et. al. | arxiv-cs.CV | 2021-07-07 |
| 486 | Neural Natural Language Processing for Unstructured Data in Electronic Health Records: A Review Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this survey paper, we summarize current neural NLP methods for EHR applications. |
IRENE LI et. al. | arxiv-cs.CL | 2021-07-06 |
| 487 | Question Answering Over Knowledge Graphs with Neural Machine Translation and Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We rather propose a KGQA approach that delegates the processing of entities to entity linking (EL) systems. |
Daniel Diomedi; Aidan Hogan; | arxiv-cs.AI | 2021-07-06 |
| 488 | No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a no-reference (NR) quality assessment metric for colored 3D models represented by both point cloud and mesh. |
ZICHENG ZHANG et. al. | arxiv-cs.CV | 2021-07-05 |
| 489 | A Knowledge-based Approach for Answering Complex Questions in Persian Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a knowledge-based approach for answering Persian complex questions using Farsbase; the Persian knowledge graph, exploiting PeCoQ; the newly created complex Persian question dataset. |
Romina Etezadi; Mehrnoush Shamsfard; | arxiv-cs.CL | 2021-07-05 |
| 490 | Mind Your Outliers! Investigating The Negative Impact of Outliers on Active Learning for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers — groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). |
Siddharth Karamcheti; Ranjay Krishna; Li Fei-Fei; Christopher D. Manning; | arxiv-cs.CL | 2021-07-05 |
| 491 | Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a dynamic working memory based cognitive VCR network, which stores accumulated commonsense between sentences to provide prior knowledge for inference. |
XUEJIAO TANG et. al. | arxiv-cs.CV | 2021-07-04 |
| 492 | Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Towards the issue of introducing related knowledge, we propose a semantic-driven knowledge-aware QA framework, which controls the knowledge injection in a coarse-to-careful fashion. |
Luxi Xing; Yue Hu; Jing Yu; Yuqiang Xie; Wei Peng; | arxiv-cs.CL | 2021-07-04 |
| 493 | Zero-Shot Estimation of Base Models’ Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we leverage a zero-shot weighted ensemble method for improving the robustness of out-of-domain generalization in MRC models. |
Razieh Baradaran; Hossein Amirkhani; | arxiv-cs.CL | 2021-06-30 |
| 494 | Overview of BioASQ 2020: The Eighth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an overview of the eighth edition of the BioASQ challenge, which ran as a lab in the Conference and Labs of the Evaluation Forum (CLEF) 2020. |
ANASTASIOS NENTIDIS et. al. | arxiv-cs.CL | 2021-06-28 |
| 495 | Adventurer’s Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering Based on Scene Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the expressed goal of improving system transparency and visual grounding in the reasoning process in VQA, we present a modular system for the task of compositional VQA based on scene graphs. |
Daniel Reich; Felix Putze; Tanja Schultz; | arxiv-cs.CV | 2021-06-28 |
| 496 | PeCoQ: A Dataset for Persian Complex Question Answering Over Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we discuss the dataset’s characteristics and describe our methodology for building it. |
Romina Etezadi; Mehrnoush Shamsfard; | arxiv-cs.CL | 2021-06-27 |
| 497 | Saying The Unseen: Video Descriptions Via Dialog Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As a step towards the more practical application scenarios, we introduce a novel task that aims to describe a video using the natural language dialog between two agents as a supplementary information source given incomplete visual data. |
Ye Zhu; Yu Wu; Yi Yang; Yan Yan; | arxiv-cs.CV | 2021-06-26 |
| 498 | Cross-domain Knowledge Distillation for Retrieval-based Question Answering Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence in this work, we provide a new perspective on the potential of the teacher-student paradigm facilitating cross-domain transfer learning, where the teacher and student tasks belong to heterogeneous domains, with the goal to improve the student model’s performance in the target domain. |
CEN CHEN et. al. | www | 2021-06-25 |
| 499 | Multilingual COVID-QA: Learning Towards Global Information Sharing Via Web Question Answering in Multiple Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multilingual COVID-QA model to answer people’s questions in their own languages while the model is able to absorb knowledge from other languages. |
RUI YAN et. al. | www | 2021-06-25 |
| 500 | Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Toward reaching this goal we propose an object-oriented reasoning approach in that video is abstracted as a dynamic stream of interacting objects. |
Long Hoang Dang; Thao Minh Le; Vuong Le; Truyen Tran; | arxiv-cs.CV | 2021-06-25 |
| 501 | A Linguistic Study on Relevance Modeling in Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We proposed three intervention methods to investigate how to leverage different modeling focuses of relevance to improve these IR tasks. |
YIXING FAN et. al. | www | 2021-06-25 |
| 502 | A Workflow Analysis of Context-driven Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. |
Shengnan Lyu; Arpit Rana; Scott Sanner; Mohamed Reda Bouadjenek; | www | 2021-06-25 |
| 503 | ComQA: Compositional Question Answering Via Hierarchical Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a large-scale compositional question answering dataset containing more than 120k human-labeled questions. |
Bingning Wang; Ting Yao; Weipeng Chen; Jingfang Xu; Xiaochuan Wang; | www | 2021-06-25 |
| 504 | Joint Spatio-Textual Reasoning for Answering Tourism Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. |
Danish Contractor; Shashank Goel; Mausam; Parag Singla; | www | 2021-06-25 |
| 505 | A Picture May Be Worth A Hundred Words for Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to take description-question pairs as input, instead of deep visual features, and fed them into a language-only Transformer model, simplifying the process and the computational cost. |
YUSUKE HIROTA et. al. | arxiv-cs.CV | 2021-06-25 |
| 506 | Weakly-Supervised Question Answering with Effective Rank and Weighted Loss Over Candidates Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an effective method to learn a question answering model in a weak supervision way. |
Haozhe Qin; Jiangang Zhu; Beijun Shen; | www | 2021-06-25 |
| 507 | Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d., compositional, and zero-shot. |
YU GU et. al. | www | 2021-06-25 |
| 508 | ParaLaw Nets — Cross-lingual Sentence-level Pretraining for Legal Text Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: From that observation, in this paper, we propose ParaLaw Nets, a pretrained model family using sentence-level cross-lingual information to reduce ambiguity and increase the performance in legal text processing. |
HA-THANH NGUYEN et. al. | arxiv-cs.CL | 2021-06-24 |
| 509 | VOGUE: Answer Verbalization Through Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this context, we propose a multi-task-based answer verbalization framework: VOGUE (Verbalization thrOuGh mUlti-task lEarning). |
Endri Kacupaj; Shyamnath Premnadh; Kuldeep Singh; Jens Lehmann; Maria Maleshkova; | arxiv-cs.CL | 2021-06-24 |
| 510 | AIT-QA: Question Answering Dataset Over Complex Tables in The Airline Industry Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we introduce the domain-specific Table QA dataset AIT-QA (Airline Industry Table QA). |
YANNIS KATSIS et. al. | arxiv-cs.CL | 2021-06-24 |
| 511 | PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To facilitate supervised learning of human rationales, here we present PALRACE (Pruned And Labeled RACE), a new MRC dataset with human labeled rationales for 800 passages selected from the RACE dataset. |
JIAJIE ZOU et. al. | arxiv-cs.CL | 2021-06-23 |
| 512 | Fine-tune The Entire RAG Architecture (including DPR Retriever) for Question-Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we illustrate how to fine-tune the entire Retrieval Augment Generation (RAG) architecture in an end-to-end manner. |
Shamane Siriwardhana; Rivindu Weerasekera; Elliott Wen; Suranga Nanayakkara; | arxiv-cs.IR | 2021-06-21 |
| 513 | Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel and easy-to-apply data augmentation strategy, namely Bilateral Generation (BiG), with a contrastive training objective for improving the performance of ranking question answer pairs with existing labeled data. |
Yang Deng; Wenxuan Zhang; Wai Lam; | arxiv-cs.CL | 2021-06-21 |
| 514 | VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address the importance of \textit{visual question answering (VQA)} task for post-disaster damage assessment by presenting our recently developed VQA dataset called \textit{HurMic-VQA} collected during hurricane Michael, and comparing the performances of baseline VQA models. |
Argho Sarkar; Maryam Rahnemoonfar; | arxiv-cs.CV | 2021-06-19 |
| 515 | Weakly Supervised Pre-Training for Multi-Hop Retriever Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issue, we propose a new method for weakly supervised multi-hop retriever pre-training without human efforts. |
Yeon Seonwoo; Sang-Woo Lee; Ji-Hoon Kim; Jung-Woo Ha; Alice Oh; | arxiv-cs.CL | 2021-06-18 |
| 516 | SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose SPBERT, a transformer-based language model pre-trained on massive SPARQL query logs. |
Hieu Tran; Long Phan; James Anibal; Binh T. Nguyen; Truong-Son Nguyen; | arxiv-cs.CL | 2021-06-18 |
| 517 | LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even scores resulting from previous EL methods, thus improving on such methods. |
HANG JIANG et. al. | arxiv-cs.CL | 2021-06-17 |
| 518 | Probing Image-Language Transformers for Verb Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To do so, we collect a dataset of image-sentence pairs (in English) consisting of 421 verbs that are either visual or commonly found in the pretraining data (i.e., the Conceptual Captions dataset). |
Lisa Anne Hendricks; Aida Nematzadeh; | arxiv-cs.CL | 2021-06-16 |
| 519 | An Intelligent Question Answering System Based on Power Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This article introduced a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power. |
YACHEN TANG et. al. | arxiv-cs.AI | 2021-06-16 |
| 520 | Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a hybrid (lexical and dense) retrieval approach that is highly competitive with the state-of-the-art dense retrieval models, while requiring substantially less computational resources. |
Georgios Sidiropoulos; Nikos Voskarides; Svitlana Vakulenko; Evangelos Kanoulas; | arxiv-cs.IR | 2021-06-15 |
| 521 | Question Answering Infused Pre-training of General-Purpose Contextualized Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a pre-training objective based on question answering (QA) for learning general-purpose contextual representations, motivated by the intuition that the representation of a phrase in a passage should encode all questions that the phrase can answer in context. |
Robin Jia; Mike Lewis; Luke Zettlemoyer; | arxiv-cs.CL | 2021-06-15 |
| 522 | How Modular Should Neural Module Networks Be for Systematic Generalization? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate that the degree of modularity of the NMN have large influence on systematic generalization. |
Vanessa D’Amario; Tomotake Sasaki; Xavier Boix; | arxiv-cs.LG | 2021-06-15 |
| 523 | Exploiting Sentence-Level Representations for Passage Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explicitly model the sentence-level representations by using Dynamic Memory Networks (DMNs) and conduct empirical evaluation to show improvements in passage re-ranking over fine-tuned vanilla BERT models by memory-enhanced explicit sentence modelling on a diverse set of open-domain QA datasets. |
Jurek Leonhardt; Fabian Beringer; Avishek Anand; | arxiv-cs.IR | 2021-06-14 |
| 524 | NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. |
Junbin Xiao; Xindi Shang; Angela Yao; Tat-Seng Chua; | cvpr | 2021-06-11 |
| 525 | AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Action Genome Question Answering (AGQA), a new benchmark for compositional spatio-temporal reasoning. |
Madeleine Grunde-McLaughlin; Ranjay Krishna; Maneesh Agrawala; | cvpr | 2021-06-11 |
| 526 | Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the principles of software metamorphic testing, we introduce MetaVQA, a model-agnostic framework for benchmarking perception capability of VQA models. |
Yuanyuan Yuan; Shuai Wang; Mingyue Jiang; Tsong Yueh Chen; | cvpr | 2021-06-11 |
| 527 | Domain-Robust VQA With Diverse Datasets and Methods But No Target Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Second, we test the robustness of different families of VQA methods (classic two-stream, transformer, and neuro-symbolic methods) to these shifts. Third, we test the applicability of existing domain adaptation methods and devise a new one to bridge VQA domain gaps, adjusted to specific VQA models. |
Mingda Zhang; Tristan Maidment; Ahmad Diab; Adriana Kovashka; Rebecca Hwa; | cvpr | 2021-06-11 |
| 528 | How Transfer |