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. For users searching for papers/patents/grants with highlights, related papers, patents, grants, experts and organizations, please try our search console. We also provide an expert tracking service to track new papers, patents and grants by 19M experts across the world.
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TABLE 1: Paper Digest: Recent Papers on Question Answering
| Paper | Author(s) | Source | Date | |
|---|---|---|---|---|
| 1 | 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 |
| 2 | Can Explanations Be Useful for Calibrating Black Box Models? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: One often wants to take an existing, trained NLP model and use it on data from a new domain. |
Xi Ye; Greg Durrett; | arxiv-cs.CL | 2021-10-14 |
| 3 | Representation Decoupling for Open-Domain Passage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to solve it with a representation decoupling method, by decoupling the passage representations into contextual sentence-level ones, and design specific CL strategies to mediate these conflicts. |
Bohong Wu; Zhuosheng Zhang; Jinyuan Wang; Hai Zhao; | arxiv-cs.CL | 2021-10-14 |
| 4 | 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 |
| 5 | 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 |
| 6 | 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 |
| 7 | Systematic Inequalities in Language Technology Performance Across The World’s Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
Damián Blasi; Antonios Anastasopoulos; Graham Neubig; | arxiv-cs.CL | 2021-10-13 |
| 8 | 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 |
| 9 | 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 |
| 10 | 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 |
| 11 | 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 |
| 12 | 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 |
| 13 | 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 |
| 14 | 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 |
| 15 | 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 |
| 16 | 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 |
| 17 | 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 |
| 18 | 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 |
| 19 | 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 |
| 20 | 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 |
| 21 | 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 |
| 22 | The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As an example, we propose kNN-Pretraining: we show that including semantically related non-neighboring sentences in the same pretraining example yields improved sentence representations and open domain question answering abilities. |
YOAV LEVINE et. al. | arxiv-cs.CL | 2021-10-09 |
| 23 | 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 |
| 24 | Query Adaptive Few-Shot Object Detection With Heterogeneous Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel FSOD model using heterogeneous graph convolutional networks. |
Guangxing Han; Yicheng He; Shiyuan Huang; Jiawei Ma; Shih-Fu Chang; | iccv | 2021-10-08 |
| 25 | 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 |
| 26 | 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 |
| 27 | 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 |
| 28 | Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a new paradigm, Calibrating Concepts and Operations (CCO), which enables neural symbolic models to capture underlying data characteristics and to reason with hierarchical importance. |
ZHUOWAN LI et. al. | iccv | 2021-10-08 |
| 29 | Generic Attention-Model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the first method to explain prediction by any Transformer-based architecture, including bi-modal Transformers and Transformers with co-attentions. |
Hila Chefer; Shir Gur; Lior Wolf; | iccv | 2021-10-08 |
| 30 | 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 |
| 31 | 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 |
| 32 | 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 |
| 33 | 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 |
| 34 | 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 |
| 35 | 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 |
| 36 | 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 |
| 37 | 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 |
| 38 | Compressing Visual-Linguistic Model Via Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study knowledge distillation(KD) to effectively compress a transformer-based large VL model into a small VL model. |
ZHIYUAN FANG et. al. | iccv | 2021-10-08 |
| 39 | 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 |
| 40 | MDETR – Modulated Detection for End-to-End Multi-Modal Understanding 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 |
| 41 | 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 |
| 42 | 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 |
| 43 | 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 |
| 44 | 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 |
| 45 | 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 |
| 46 | Adversarial VQA: A New Benchmark for Evaluating The Robustness of VQA Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To study this, we introduce Adversarial VQA, a new large-scale VQA benchmark, collected iteratively via an adversarial human-and-model-in-the-loop procedure. |
Linjie Li; Jie Lei; Zhe Gan; Jingjing Liu; | iccv | 2021-10-08 |
| 47 | 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 |
| 48 | 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 |
| 49 | Towards Continual Knowledge Learning of Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To push the community towards better maintenance of ever-changing LMs, we formulate a new continual learning (CL) problem called Continual Knowledge Learning (CKL). We construct a new benchmark and metric to quantify the retention of time-invariant world knowledge, the update of outdated knowledge, and the acquisition of new knowledge. |
JOEL JANG et. al. | arxiv-cs.CL | 2021-10-07 |
| 50 | 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 |
| 51 | 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 |
| 52 | 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 |
| 53 | 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 |
| 54 | 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 |
| 55 | 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 |
| 56 | 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 |
| 57 | 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 |
| 58 | 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 |
| 59 | TopiOCQA: Open-domain Conversational Question Answeringwith 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 |
| 60 | 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 Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. |
Francesco Sovrano; Fabio Vitali; | arxiv-cs.HC | 2021-10-02 |
| 61 | Asking Questions on Handwritten Document Collections IF:2 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 |
| 62 | Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a new paradigm, Calibrating Concepts and Operations (CCO), which enables neural symbolic models to capture underlying data characteristics and to reason with hierarchical importance. Additionally, we create a perturbed test set for better understanding and analyzing model performance on real images. |
ZHUOWAN LI et. al. | arxiv-cs.CV | 2021-10-01 |
| 63 | 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 |
| 64 | 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 |
| 65 | Single-dataset Experts for Multi-dataset Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We find that these Multi-Adapter Dataset Experts (MADE) outperform all our baselines in terms of in-distribution accuracy, and simple methods based on parameter-averaging lead to better zero-shot generalization and few-shot transfer performance, offering a strong and versatile starting point for building new reading comprehension systems. |
Dan Friedman; Ben Dodge; Danqi Chen; | arxiv-cs.CL | 2021-09-28 |
| 66 | Text Simplification for Comprehension-based Question-Answering IF:2 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 |
| 67 | Context-guided Triple Matching for Multiple Choice Question Answering IF:2 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 |
| 68 | An Empirical Study of Question Discussions on Stack Overflow IF:2 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 |
| 69 | VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering IF:2 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 |
| 70 | 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 |
| 71 | 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 |
| 72 | 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 |
| 73 | Entity Linking Meets Deep Learning: Techniques and Solutions IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this survey, we present a comprehensive review and analysis of existing DL based EL methods. Later, we introduce ten commonly used EL data sets and give a quantitative performance analysis of DL based EL methods over these data sets. |
WEI SHEN et. al. | arxiv-cs.CL | 2021-09-26 |
| 74 | QA-Align: Representing Cross-Text Content Overlap By Aligning Question-Answer Propositions IF:2 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; | arxiv-cs.CL | 2021-09-26 |
| 75 | 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 |
| 76 | Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering IF:2 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 |
| 77 | More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering IF:2 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 |
| 78 | 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 |
| 79 | 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 |
| 80 | Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap IF:2 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 |
| 81 | 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 |
| 82 | Recursively Summarizing Books with Human Feedback IF:2 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 |
| 83 | BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles IF:2 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 |
| 84 | NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset IF:2 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 |
| 85 | 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 |
| 86 | 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 |
| 87 | A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in The OBQA Context IF:2 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 |
| 88 | RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we are applying retrieval-based modeling ideas to the problem of multi-domain task-oriented semantic parsing for conversational assistants. |
Vivek Gupta; Akshat Shrivastava; Adithya Sagar; Armen Aghajanyan; Denis Savenkov; | arxiv-cs.CL | 2021-09-21 |
| 89 | What Would It Take to Get Biomedical QA Systems Into Practice? IF:2 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 |
| 90 | 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 |
| 91 | Salience-Aware Event Chain Modeling for Narrative Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. |
Xiyang Zhang; Muhao Chen; Jonathan May; | arxiv-cs.CL | 2021-09-21 |
| 92 | 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 |
| 93 | 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 |
| 94 | Modality and Negation in Event Extraction IF:2 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 |
| 95 | 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 |
| 96 | CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study generating abstractive summaries that are faithful and factually consistent with the given articles. |
Shuyang Cao; Lu Wang; | arxiv-cs.CL | 2021-09-19 |
| 97 | Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona IF:2 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 |
| 98 | Complex Temporal Question Answering on Knowledge Graphs IF:2 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 |
| 99 | 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 |
| 100 | Simple Entity-Centric Questions Challenge Dense Retrievers IF:2 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; | arxiv-cs.CL | 2021-09-17 |
| 101 | RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering IF:2 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 |
| 102 | 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 |
| 103 | Task-adaptive Pre-training of Language Models with Word Embedding Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel fine-tuning process: task-adaptive pre-training with word embedding regularization (TAPTER). |
Kosuke Nishida; Kyosuke Nishida; Sen Yoshida; | arxiv-cs.CL | 2021-09-17 |
| 104 | Language Models As A Knowledge Source for Cognitive Agents IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To illustrate, we introduce a hypothetical robot agent and describe how language models could extend its task knowledge and improve its performance and the kinds of knowledge and methods the agent can use to exploit the knowledge within a language model. |
III Robert E. Wray; James R. Kirk; John E. Laird; | arxiv-cs.AI | 2021-09-16 |
| 105 | Phrase Retrieval Learns Passage Retrieval, Too IF:2 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; | arxiv-cs.CL | 2021-09-16 |
| 106 | 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 |
| 107 | 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 |
| 108 | Optimising Rolling Stock Planning Including Maintenance with Constraint Programming and Quantum Annealing IF:2 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 |
| 109 | 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 |
| 110 | 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 |
| 111 | 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 |
| 112 | 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 |
| 113 | Will This Question Be Answered? Question Filtering Via Answer Model Distillation for Efficient Question Answering IF:2 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; | arxiv-cs.CL | 2021-09-14 |
| 114 | Broaden The Vision: Geo-Diverse Visual Commonsense Reasoning IF:2 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; | arxiv-cs.CL | 2021-09-14 |
| 115 | Adaptive Information Seeking for Open-Domain Question Answering IF:2 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; | arxiv-cs.CL | 2021-09-14 |
| 116 | Question Answering Over Electronic Devices: A New Benchmark Dataset and A Multi-Task Learning Based QA Framework IF:2 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 |
| 117 | End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs IF:2 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 |
| 118 | XGQA: Cross-Lingual Visual Question Answering IF:2 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 |
| 119 | SituatedQA: Incorporating Extra-Linguistic Contexts Into QA IF:2 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 |
| 120 | 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 |
| 121 | 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: In this paper, we take a drastically different approach. |
Jihyung Kil; Cheng Zhang; Dong Xuan; Wei-Lun Chao; | arxiv-cs.CV | 2021-09-13 |
| 122 | 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; | arxiv-cs.CL | 2021-09-11 |
| 123 | Refocusing on Relevance: Personalization in NLG IF:2 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; | arxiv-cs.CL | 2021-09-10 |
| 124 | An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA IF:2 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 |
| 125 | Generating Self-Contained and Summary-Centric Question Answer Pairs Via Differentiable Reward Imitation Learning IF:2 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; | arxiv-cs.CL | 2021-09-10 |
| 126 | Counterfactual Adversarial Learning with Representation Interpolation IF:2 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 |
| 127 | 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 |
| 128 | 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 |
| 129 | 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. | arxiv-cs.CL | 2021-09-10 |
| 130 | Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs Into BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach that can handle a very large knowledge graph (KG) by partitioning it into smaller sub-graphs and infusing their specific knowledge into various BERT models using lightweight adapters. |
Zaiqiao Meng; Fangyu Liu; Thomas Hikaru Clark; Ehsan Shareghi; Nigel Collier; | arxiv-cs.CL | 2021-09-10 |
| 131 | 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 |
| 132 | ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization IF:2 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; | arxiv-cs.CL | 2021-09-09 |
| 133 | Zero-Shot Dialogue State Tracking Via Cross-Task Transfer IF:2 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 |
| 134 | Towards Developing A Multilingual and Code-Mixed Visual Question Answering System By Knowledge Distillation IF:2 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 |
| 135 | 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 |
| 136 | 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 |
| 137 | Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering IF:2 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; | arxiv-cs.CL | 2021-09-08 |
| 138 | Formal Query Building with Query Structure Prediction for Complex Question Answering Over Knowledge Base IF:2 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 |
| 139 | R2-D2: A Modular Baseline for Open-Domain Question Answering IF:2 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 |
| 140 | It Is AI’s Turn to Ask Human A Question: Question and Answer Pair Generation for Children Storybooks in FairytaleQA Dataset IF:2 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 |
| 141 | Cross-Policy Compliance Detection Via Question Answering IF:2 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; | arxiv-cs.CL | 2021-09-08 |
| 142 | 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 |
| 143 | Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering IF:2 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 |
| 144 | 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 |
| 145 | Improved RAMEN: Towards Domain Generalization for Visual Question Answering IF:2 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 |
| 146 | DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this work, we present a pre-training framework for long dialogue understanding and summarization. |
Ming Zhong; Yang Liu; Yichong Xu; Chenguang Zhu; Michael Zeng; | arxiv-cs.CL | 2021-09-06 |
| 147 | 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 |
| 148 | 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 |
| 149 | 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 |
| 150 | FewshotQA: A Simple Framework for Few-shot Learning of Question Answering Tasks Using Pre-trained Text-to-text Models IF:2 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; | arxiv-cs.CL | 2021-09-04 |
| 151 | Learning Neural Models for Natural Language Processing in The Face of Distributional Shift IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The first goal of this thesis is to characterize the different forms this shift can take in the context of natural language processing, and propose benchmarks and evaluation metrics to measure its effect on current deep learning architectures. |
Paul Michel; | arxiv-cs.CL | 2021-09-03 |
| 152 | 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 |
| 153 | Challenges in Generalization in Open Domain Question Answering IF:2 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 |
| 154 | WebQA: Multihop and Multimodal QA IF:2 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 |
| 155 | Does Knowledge Help General NLU? An Empirical Study IF:2 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 |
| 156 | Structured Context and High-Coverage Grammar for Conversational Question Answering Over Knowledge Graphs IF:2 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 |
| 157 | When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions IF:2 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 |
| 158 | FinQA: A Dataset of Numerical Reasoning Over Financial Data IF:2 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. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. |
ZHIYU CHEN et. al. | arxiv-cs.CL | 2021-08-31 |
| 159 | Unsupervised Open-Domain Question Answering IF:2 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 |
| 160 | Effectiveness of Deep Networks in NLP Using BiDAF As An Example Architecture Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this context, I explored the effectiveness of deep networks focussing on the model encoder layer of BiDAF. |
Soumyendu Sarkar; | arxiv-cs.CL | 2021-08-31 |
| 161 | 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; | arxiv-cs.CL | 2021-08-31 |
| 162 | Semi-Supervised Exaggeration Detection of Health Science Press Releases IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. |
Dustin Wright; Isabelle Augenstein; | arxiv-cs.CL | 2021-08-30 |
| 163 | On The Multilingual Capabilities of Very Large-Scale English Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the multilingual skills of GPT-3, focusing on one language that barely appears in the pre-training corpus, Catalan, which makes the results especially meaningful; we assume that our results may be relevant for other languages as well. |
Jordi Armengol-Estapé; Ona de Gibert Bonet; Maite Melero; | arxiv-cs.CL | 2021-08-30 |
| 164 | Generating Answer Candidates for Quizzes and Answer-Aware Question Generators IF:2 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 |
| 165 | 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 |
| 166 | Query-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 Questions IF:2 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 |
| 167 | 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 |
| 168 | Dealing with Typos for BERT-based Passage Retrieval and Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we consider the Dense Retriever (DR), a passage retrieval method, and the BERT re-ranker, a popular passage re-ranking method. |
Shengyao Zhuang; Guido Zuccon; | arxiv-cs.IR | 2021-08-27 |
| 169 | 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 |
| 170 | Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an algorithm for sentence ordering in a corpus of short stories. |
Melika Golestani; Seyedeh Zahra Razavi; Zeinab Borhanifard; Farnaz Tahmasebian; Hesham Faili; | arxiv-cs.CL | 2021-08-24 |
| 171 | SimVLM: Simple Visual Language Model Pretraining with Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we relax these constraints and present a minimalist pretraining framework, named Simple Visual Language Model (SimVLM). |
ZIRUI WANG et. al. | arxiv-cs.CV | 2021-08-24 |
| 172 | Sequence-to-Sequence Learning on Keywords for Efficient FAQ Retrieval IF:2 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 |
| 173 | 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 |
| 174 | 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 |
| 175 | UNIQORN: Unified Question Answering Over RDF Knowledge Graphs and Natural Language Text IF:2 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 |
| 176 | Localize, Group, and Select: Boosting Text-VQA By Scene Text Modeling IF:2 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 |
| 177 | SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining IF:2 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 |
| 178 | Efficient Contextualization Using Top-k Operators for Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents ECQA, 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 |
| 179 | The Multi-Modal Video Reasoning and Analyzing Competition IF:2 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 |
| 180 | VerSaChI: Finding Statistically Significant Subgraph Matches Using Chebyshev’s Inequality IF:2 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 |
| 181 | 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 |
| 182 | X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose X-modaler — a versatile and high-performance codebase that encapsulates the state-of-the-art cross-modal analytics into several general-purpose stages (e.g., pre-processing, encoder, cross-modal interaction, decoder, and decode strategy). |
Yehao Li; Yingwei Pan; Jingwen Chen; Ting Yao; Tao Mei; | arxiv-cs.CV | 2021-08-18 |
| 183 | Generative Relation Linking for Question Answering Over Knowledge Bases IF:2 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 |
| 184 | Complex Knowledge Base Question Answering: A Survey IF:2 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 |
| 185 | HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation IF:2 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 |
| 186 | Proposal-free One-stage Referring Expression Via Grid-Word Cross-Attention IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a proposal-free one-stage (PFOS) model that is able to regress the region-of-interest from the image, based on a textual query, in an end-to-end manner. |
Wei Suo; MengYang Sun; Peng Wang; Qi Wu; | ijcai | 2021-08-13 |
| 187 | Automatically Paraphrasing Via Sentence Reconstruction and Round-trip Translation IF:2 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 |
| 188 | MRD-Net: Multi-Modal Residual Knowledge Distillation for Spoken Question Answering IF:2 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 |
| 189 | A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions IF:2 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 |
| 190 | A Dataset for Answering Time-Sensitive Questions IF:2 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 |
| 191 | AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss IF:2 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 |
| 192 | Semantic Answer Similarity for Evaluating Question Answering Models IF:2 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 |
| 193 | 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 |
| 194 | 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 |
| 195 | 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 |
| 196 | M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for Unified Pretraining IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the largest dataset for pretraining in Chinese, which consists of over 1.9TB images and 292GB texts. |
JUNYANG LIN et. al. | kdd | 2021-08-12 |
| 197 | Neural-Answering Logical Queries on Knowledge Graphs IF:2 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 |
| 198 | Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the opportunities and challenges, in this paper we describe our novel practice in Microsoft to leverage such large amounts of unlabeled data in target languages in real production settings. |
SHINING LIANG et. al. | kdd | 2021-08-12 |
| 199 | AliCoCo2: Commonsense Knowledge Extraction, Representation and Application in E-commerce IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a multi-task encoder-decoder framework to provide effective representations for nodes and edges from AliCoCo2. |
XUSHENG LUO et. al. | kdd | 2021-08-12 |
| 200 | PAM: Understanding Product Images in Cross Product Category Attribute Extraction IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes a more inclusive framework that fully utilizes these different modalities for attribute extraction.Inspired by recent works in visual question answering, we use a transformer based sequence to sequence model to fuse representations of product text, Optical Character Recognition (OCR) tokens and visual objects detected in the product image. |
RONGMEI LIN et. al. | kdd | 2021-08-12 |
| 201 | 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 |
| 202 | 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 |
| 203 | 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 |
| 204 | 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 |
| 205 | FedMatch: Federated Learning Over Heterogeneous Question Answering Data IF:2 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 |
| 206 | Extracting Semantics from Maintenance Records IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We overcome these challenges by developing three approaches built on the foundation of domain expert knowledge captured in dictionaries and ontologies. |
Sharad Dixit; Varish Mulwad; Abhinav Saxena; | arxiv-cs.CL | 2021-08-11 |
| 207 | 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 |
| 208 | BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the challenges of applying pre-trained V&L models in medical applications. |
MASOUD MONAJATIPOOR et. al. | arxiv-cs.CV | 2021-08-10 |
| 209 | Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models IF:2 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 |
| 210 | Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to leverage commonsense knowledge for the tasks of false news classification and check-worthy claim detection. |
Ipek Baris Schlicht; Erhan Sezerer; Selma Tekir; Oul Han; Zeyd Boukhers; | arxiv-cs.CL | 2021-08-08 |
| 211 | Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering IF:2 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 |
| 212 | 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 |
| 213 | Sparse Continuous Distributions and Fenchel-Young Losses IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For linearly parametrized families, we show that minimization of Fenchel-Young losses is equivalent to moment matching of the statistics, generalizing a fundamental property of exponential families. |
ANDRÉ F. T. MARTINS et. al. | arxiv-cs.LG | 2021-08-04 |
| 214 | 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 |
| 215 | Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management IF:2 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 |
| 216 | Extending Sticky-Datalog+/- Via Finite-Position Selection Functions: Tractability, Algorithms, and Optimization IF:2 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 |
| 217 | MuSiQue: Multi-hop Questions Via Single-hop Question Composition IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To build challenging multi-hop question answering datasets, we propose a bottom-up semi-automatic process of constructing multi-hop question via composition of single-hop questions. We also build an even more challenging dataset, MuSiQue-Full, consisting of answerable and unanswerable contrast question pairs, where model performance drops further by 13+ F1 pts. |
Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal; | arxiv-cs.CL | 2021-08-01 |
| 218 | 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 |
| 219 | 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 |
| 220 | Talk2Data: High-Level Question Decomposition for Data-Oriented Question and Answering IF:2 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 |
| 221 | An Online Question Answering System Based on Sub-graph Searching IF:2 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 |
| 222 | QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension IF:2 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 |
| 223 | Multi-Scale Progressive Attention Network for Video Question Answering IF:2 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 |
| 224 | Answering Ambiguous Questions Through Generative Evidence Fusion and Round-Trip Prediction IF:2 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 |
| 225 | Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction IF:2 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 |
| 226 | Evaluating Entity Disambiguation and The Role of Popularity in Retrieval-Based NLP IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Retrieval (AmbER) sets. |
Anthony Chen; Pallavi Gudipati; Shayne Longpre; Xiao Ling; Sameer Singh; | acl | 2021-07-26 |
| 227 | Privacy at Scale: Introducing The PrivaSeer Corpus of Web Privacy Policies IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a corpus creation pipeline with stages that include a web crawler, language detection, document classification, duplicate and near-duplicate removal, and content extraction. |
Mukund Srinath; Shomir Wilson; C Lee Giles; | acl | 2021-07-26 |
| 228 | Continuous Language Generative Flow IF:2 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 |
| 229 | Guiding The Growth: Difficulty-Controllable Question Generation Through Step-by-Step Rewriting IF:2 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 |
| 230 | Mind Your Outliers! Investigating The Negative Impact of Outliers on Active Learning for Visual Question Answering IF:2 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 |
| 231 | Discrete Cosine Transform As Universal Sentence Encoder IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we utilize DCT encoder to generate universal sentence representation for different languages such as German, French, Spanish and Russian. |
Nada Almarwani; Mona Diab; | acl | 2021-07-26 |
| 232 | Neural Semi-Markov CRF for Monolingual Word Alignment IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. |
Wuwei Lan; Chao Jiang; Wei Xu; | acl | 2021-07-26 |
| 233 | Learning Dense Representations of Phrases at Scale IF:2 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 |
| 234 | A Semantic-based Method for Unsupervised Commonsense Question Answering IF:2 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 |
| 235 | Robustifying Multi-hop QA Through Pseudo-Evidentiality Training IF:2 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 |
| 236 | Comparing Test Sets with Item Response Theory IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To measure this uniformly across datasets, we draw on Item Response Theory and evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. |
CLARA VANIA et. al. | acl | 2021-07-26 |
| 237 | Question Answering Over Temporal Knowledge Graphs IF:2 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 |
| 238 | XMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering IF:2 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 |
| 239 | ABCD: A Graph Framework to Convert Complex Sentences to A Covering Set of Simple Sentences IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. |
Yanjun Gao; Ting-Hao Huang; Rebecca J. Passonneau; | acl | 2021-07-26 |
| 240 | VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. |
FULI LUO et. al. | acl | 2021-07-26 |
| 241 | DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an entity summary description. |
Weijia Shi; Mandar Joshi; Luke Zettlemoyer; | acl | 2021-07-26 |
| 242 | StructuralLM: Structural Pre-training for Form Understanding IF:2 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 |
| 243 | Multi-Task Retrieval for Knowledge-Intensive Tasks IF:2 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 |
| 244 | An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To avoid the disadvantages of existing models and exploit the generalized representation across the two tasks, we design an end-to-end progressive multi-task learning model for jointly modeling medical NER and NEN in an effective way. |
Baohang Zhou; Xiangrui Cai; Ying Zhang; Xiaojie Yuan; | acl | 2021-07-26 |
| 245 | ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models Via Contrastive Learning IF:2 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 |
| 246 | CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction IF:2 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 |
| 247 | Syntax-Enhanced Pre-trained Model IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. |
ZENAN XU et. al. | acl | 2021-07-26 |
| 248 | Consistency Regularization for Cross-Lingual Fine-Tuning IF:2 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 |
| 249 | A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding IF:2 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 |
| 250 | Pre-training Universal Language Representation IF:2 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 |
| 251 | ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data IF:2 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 |
| 252 | Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering IF:2 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 |
| 253 | A Mutual Information Maximization Approach for The Spurious Solution Problem in Weakly Supervised Question Answering IF:2 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 |
| 254 | Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we extend PoWER-BERT (Goyal et al., 2020) and propose Length-Adaptive Transformer that can be used for various inference scenarios after one-shot training. |
Gyuwan Kim; Kyunghyun Cho; | acl | 2021-07-26 |
| 255 | Coreference Reasoning in Machine Reading Comprehension IF:2 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 |
| 256 | Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards IF:2 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 |
| 257 | Towards More Equitable Question Answering Systems: How Much More Data Do You Need? IF:2 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 |
| 258 | End-to-End Training of Neural Retrievers for Open-Domain Question Answering IF:2 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 |
| 259 | GTM: A Generative Triple-wise Model for Conversational Question Generation IF:2 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 |
| 260 | UnitedQA: A Hybrid Approach for Open Domain Question Answering IF:2 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 |
| 261 | Efficient Passage Retrieval with Hashing for Open-domain Question Answering IF:2 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 |
| 262 | LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we take a different, neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. |
HANG JIANG et. al. | acl | 2021-07-26 |
| 263 | A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections IF:2 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 |
| 264 | Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering IF:2 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 |
| 265 | TAT-QA: A Question Answering Benchmark on A Hybrid of Tabular and Textual Content in Finance IF:2 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 |
| 266 | On The Efficacy of Adversarial Data Collection for Question Answering: Results from A Large-Scale Randomized Study IF:2 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 |
| 267 | Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering IF:2 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 |
| 268 | KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we examine the challenges that still prevent these techniques from practical deployment. |
Chia-Hsuan Lee; Oleksandr Polozov; Matthew Richardson; | acl | 2021-07-26 |
| 269 | Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a framework for computationally measuring uptake, by (1) releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake by experts; (2) formalizing uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next utterance classification; (3) conducting a linguistically-motivated comparison of different unsupervised measures and (4) correlating these measures with educational outcomes. |
DOROTTYA DEMSZKY et. al. | acl | 2021-07-26 |
| 270 | Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? IF:2 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 |
| 271 | Learning to Perturb Word Embeddings for Out-of-distribution QA IF:2 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 |
| 272 | Syntax-augmented Multilingual BERT for Cross-lingual Transfer IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work shows that explicitly providing language syntax and training mBERT using an auxiliary objective to encode the universal dependency tree structure helps cross-lingual transfer. |
Wasi Ahmad; Haoran Li; Kai-Wei Chang; Yashar Mehdad; | acl | 2021-07-26 |
| 273 | BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Of course, we do not have answers now, but, as an attempt to find better neural architectures and training schemes, we pretrain a simple CNN using a GAN-style learning scheme and Wikipedia data, and then integrate it with standard TLMs. |
Jong-Hoon Oh; Ryu Iida; Julien Kloetzer; Kentaro Torisawa; | acl | 2021-07-26 |
| 274 | Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints IF:2 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 |
| 275 | Few-Shot Question Answering By Pretraining Span Selection IF:2 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 |
| 276 | Learning Faithful Representations of Causal Graphs IF:2 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 |
| 277 | What Ingredients Make for An Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we compare the efficacy of interventions that have been proposed in prior work as ways of improving data quality. |
NIKITA NANGIA et. al. | acl | 2021-07-26 |
| 278 | Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation IF:2 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 |
| 279 | One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present CORA, a Cross-lingual Open-Retrieval Answer Generation model that can answer questions across many languages even when language-specific annotated data or knowledge sources are unavailable. |
Akari Asai; Xinyan Yu; Jungo Kasai; Hannaneh Hajishirzi; | arxiv-cs.CL | 2021-07-26 |
| 280 | ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this gap, we design annotation protocols motivated by an issues-viewpoints-assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. |
ALEXANDER FABBRI et. al. | acl | 2021-07-26 |
| 281 | Joint Models for Answer Verification in Question Answering Systems IF:2 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 |
| 282 | Improving Factual Consistency of Abstractive Summarization Via Question Answering IF:2 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 |
| 283 | Recursive Tree-Structured Self-Attention for Answer Sentence Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the Tree Aggregation Transformer: a novel recursive, tree-structured self-attention model for AS2. |
Khalil Mrini; Emilia Farcas; Ndapa Nakashole; | acl | 2021-07-26 |
| 284 | 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 |
| 285 | 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 |
| 286 | 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 |
| 287 | 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 |
| 288 | 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 |
| 289 | 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 |
| 290 | 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 |
| 291 | 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 |
| 292 | 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 |
| 293 | 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 |
| 294 | ERNIE-Doc: A Retrospective Long-Document Modeling Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose ERNIE-Doc, a document-level language pretraining model based on Recurrence Transformers. |
SIYU DING et. al. | acl | 2021-07-26 |
| 295 | 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 |
| 296 | COSY: COunterfactual SYntax for Cross-Lingual Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages. |
Sicheng Yu; Hao Zhang; Yulei Niu; Qianru Sun; Jing Jiang; | acl | 2021-07-26 |
| 297 | Hybrid Autoregressive Solver for Scalable Abductive Natural Language Inference IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To maximise both accuracy and inference time, we propose a hybrid abductive solver that autoregressively combines a dense bi-encoder with a sparse model of explanatory power, computed leveraging explicit patterns in the explanations. |
Marco Valentino; Mokanarangan Thayaparan; Deborah Ferreira; André Freitas; | arxiv-cs.CL | 2021-07-25 |
| 298 | X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering IF:2 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 |
| 299 | The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding IF:2 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 |
| 300 | CogME: A Novel Evaluation Metric for Video Understanding Intelligence IF:2 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 |
| 301 | CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the problem of injecting causal knowledge into pre-trained models. |
Zhongyang Li; Xiao Ding; Kuo Liao; Bing Qin; Ting Liu; | arxiv-cs.CL | 2021-07-20 |
| 302 | Separating Skills and Concepts for Novel Visual Question Answering IF:2 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 |
| 303 | Bridging The Gap Between Language Model and Reading Comprehension: Unsupervised MRC Via Self-Supervision IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new framework for unsupervised MRC. |
NING BIAN et. al. | arxiv-cs.CL | 2021-07-18 |
| 304 | A Discriminative Semantic Ranker for Question Retrieval IF:2 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 |
| 305 | Are Multilingual Models The Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on Catalan with the aim to explore to what extent a medium-sized monolingual language model is competitive with state-of-the-art large multilingual models. For this, we: (1) build a clean, high-quality textual Catalan corpus (CaText), the largest to date (but only a fraction of the usual size of the previous work in monolingual language models), (2) train a Transformer-based language model for Catalan (BERTa), and (3) devise a thorough evaluation in a diversity of settings, comprising a complete array of downstream tasks, namely, Part of Speech Tagging, Named Entity Recognition and Classification, Text Classification, Question Answering, and Semantic Textual Similarity, with most of the corresponding datasets being created ex novo. |
JORDI ARMENGOL-ESTAPÉ et. al. | arxiv-cs.CL | 2021-07-16 |
| 306 | Exploiting Rich Syntax for Better Knowledge Base Question Answering IF:2 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 |
| 307 | A Survey of Knowledge Graph Embedding and Their Applications IF:2 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 |
| 308 | Spanish Language Models IF:2 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 |
| 309 | Passage Retrieval for Outside-Knowledge Visual Question Answering IF:2 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 |
| 310 | Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading IF:2 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 |
| 311 | Learning Discriminative Neural Representations for Event Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the challenge, we propose to learn discriminative neural representations (DNR) from texts. |
Jinzhi Liao; Xiang Zhao; Xinyi Li; Lingling Zhang; Jiuyang Tang; | sigir | 2021-07-13 |
| 312 | Answer Complex Questions: Path Ranker Is All You Need IF:2 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 |
| 313 | Multi-Type Textual Reasoning for Product-Aware Answer Generation IF:2 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 |
| 314 | How Much Can CLIP Benefit Vision-and-Language Tasks? IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. |
SHENG SHEN et. al. | arxiv-cs.CV | 2021-07-13 |
| 315 | Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering IF:2 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 |
| 316 | AliMe DA: A Data Augmentation Framework for Question Answering in Cold-start Scenarios IF:2 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 |
| 317 | Synthetic Target Domain Supervision for Open Retrieval QA IF:2 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 |
| 318 | On The Orthogonality of Bias and Utility in Ad Hoc Retrieval IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we empirically study this tradeoff and explore whether it would be possible to reduce bias while maintaining similar retrieval utility. |
Amin Bigdeli; Negar Arabzadeh; Shirin Seyedsalehi; Morteza Zihayat; Ebrahim Bagheri; | sigir | 2021-07-13 |
| 319 | Answering Any-hop Open-domain Questions with Iterative Document Reranking IF:2 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 |
| 320 | Towards Building A Food Knowledge Graph for Internet of Food IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Scope and approach: We review the evolution of food knowledge organization, from food classification, food ontology to food knowledge graphs. |
Weiqing Min; Chunlin Liu; Shuqiang Jiang; | arxiv-cs.CV | 2021-07-13 |
| 321 | Event Occurrence Date Estimation Based on Multivariate Time Series Analysis Over Temporal Document Collections IF:2 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 |
| 322 | LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering IF:2 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 |
| 323 | 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 |
| 324 | PonderNet: Learning to Ponder IF:2 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 |
| 325 | Deep Neural Networks Evolve Human-like Attention Distribution During Reading Comprehension IF:2 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 |
| 326 | Zero-shot Visual Question Answering Using Knowledge Graph IF:2 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 |
| 327 | Similar Cases Recommendation Using Legal Knowledge Graphs IF:2 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 |
| 328 | DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering IF:2 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 |
| 329 | An Initial Investigation of Non-Native Spoken Question-Answering IF:2 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 |
| 330 | Benchmarking for Biomedical Natural Language Processing Tasks with A Domain Specific ALBERT IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present BioALBERT, a domain-specific adaptation of A Lite Bidirectional Encoder Representations from Transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine tuned for 6 different tasks across 20 benchmark datasets. |
Usman Naseem; Adam G. Dunn; Matloob Khushi; Jinman Kim; | arxiv-cs.CL | 2021-07-09 |
| 331 | 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 |
| 332 | Bayesian Attention Belief Networks IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces Bayesian attention belief networks, which construct a decoder network by modeling unnormalized attention weights with a hierarchy of gamma distributions, and an encoder network by stacking Weibull distributions with a deterministic-upward-stochastic-downward structure to approximate the posterior. |
Shujian Zhang; Xinjie Fan; Bo Chen; Mingyuan Zhou; | icml | 2021-07-08 |
| 333 | Reasoning Over Virtual Knowledge Bases With Open Predicate Relations IF:2 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 |
| 334 | A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a biologically inspired solution that solves this problem. |
Philipp Plank; Arjun Rao; Andreas Wild; Wolfgang Maass; | arxiv-cs.NE | 2021-07-08 |
| 335 | LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs IF:2 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 |
| 336 | Poolingformer: Long Document Modeling with Pooling Attention IF:2 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 |
| 337 | Learning to Rehearse in Long Sequence Memorization IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Rehearsal Memory (RM) to enhance long-sequence memorization by self-supervised rehearsal with a history sampler. |
ZHU ZHANG et. al. | icml | 2021-07-08 |
| 338 | Model Performance Scaling with Multiple Data Sources Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that there is a simple scaling law that predicts the loss incurred by a model even under varying dataset composition. |
Tatsunori Hashimoto; | icml | 2021-07-08 |
| 339 | MedGPT: Medical Concept Prediction from Clinical Narratives IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The data available in Electronic Health Records (EHRs) provides the opportunity to transform care, and the best way to provide better care for one patient is through learning from the data available on all other patients. |
ZELJKO KRALJEVIC et. al. | arxiv-cs.CL | 2021-07-07 |
| 340 | MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering IF:2 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 |
| 341 | Probabilistic Graph Reasoning for Natural Proof Generation IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the problem of reasoning over natural language statements. |
CHANGZHI SUN et. al. | arxiv-cs.CL | 2021-07-06 |
| 342 | SOCluster- Towards Intent-based Clustering of Stack Overflow Questions Using Graph-Based Approach IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: It is here, we propose SOCluster, an approach and a tool to cluster SO questions based on intent using a graph-based clustering approach. |
Abhishek Kumar; Deep Ghadiyali; Sridhar Chimalakonda; | arxiv-cs.SE | 2021-07-06 |
| 343 | Neural Natural Language Processing for Unstructured Data in Electronic Health Records: A Review IF:2 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 |
| 344 | Question Answering Over Knowledge Graphs with Neural Machine Translation and Entity Linking IF:2 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 |
| 345 | Mind Your Outliers! Investigating The Negative Impact of Outliers on Active Learning for Visual Question Answering IF:2 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 |
| 346 | No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models IF:2 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 |
| 347 | A Knowledge-based Approach for Answering Complex Questions in Persian IF:2 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 |
| 348 | NOTE: Solution for KDD-CUP 2021 WikiKG90M-LSC IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we train the advanced algorithms in different domains to learn the triplets, including OTE, QuatE, RotatE and TransE. |
WEIYUE SU et. al. | arxiv-cs.IR | 2021-07-05 |
| 349 | Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering IF:2 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 |
| 350 | Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory IF:2 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 |
| 351 | Revisiting The Primacy of English in Zero-shot Cross-lingual Transfer IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In our study, we compare English against other transfer languages for fine-tuning, on two pre-trained multilingual models (mBERT and mT5) and multiple classification and question answering tasks. |
Iulia Turc; Kenton Lee; Jacob Eisenstein; Ming-Wei Chang; Kristina Toutanova; | arxiv-cs.CL | 2021-06-30 |
| 352 | 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 |
| 353 | Overview of BioASQ 2020: The Eighth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering IF:2 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 |
| 354 | Adventurer’s Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering Based on Scene Graphs IF:2 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 |
| 355 | PeCoQ: A Dataset for Persian Complex Question Answering Over Knowledge Graph IF:2 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 |
| 356 | Saying The Unseen: Video Descriptions Via Dialog Agents IF:2 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 |
| 357 | Semi-Open Information Extraction IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define a new task, namely Semi-Open Information Extraction (SOIE), to address this need. |
BOWEN YU et. al. | www | 2021-06-25 |
| 358 | Advanced Semantics for Commonsense Knowledge Extraction IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. |
Tuan-Phong Nguyen; Simon Razniewski; Gerhard Weikum; | www | 2021-06-25 |
| 359 | Cross-domain Knowledge Distillation for Retrieval-based Question Answering Systems IF:2 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 |
| 360 | Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases IF:2 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 |
| 361 | Weakly-Supervised Question Answering with Effective Rank and Weighted Loss Over Candidates IF:2 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 |
| 362 | A Linguistic Study on Relevance Modeling in Information Retrieval IF:2 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 |
| 363 | Cross-lingual Language Model Pretraining for Retrieval IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce two novel retrieval-oriented pretraining tasks to further pretrain cross-lingual language models for downstream retrieval tasks such as cross-lingual ad-hoc retrieval (CLIR) and cross-lingual question answering (CLQA). |
Puxuan Yu; Hongliang Fei; Ping Li; | www | 2021-06-25 |
| 364 | Joint Spatio-Textual Reasoning for Answering Tourism Questions IF:2 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 |
| 365 | ComQA: Compositional Question Answering Via Hierarchical Graph Neural Networks IF:2 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 |
| 366 | Wiki2Prop: A Multimodal Approach for Predicting Wikidata Properties from Wikipedia IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on entities with a dedicated Wikipedia page in any language to make predictions directly based on textual content. |
Michael Luggen; Julien Audiffren; Djellel Difallah; Philippe Cudré-Mauroux; | www | 2021-06-25 |
| 367 | Search Engines Vs. Symptom Checkers: A Comparison of Their Effectiveness for Online Health Advice IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we conducted a user study with 64 real-world users performing 8 simulated self-diagnosis tasks. |
Sebastian Cross; Ahmed Mourad; Guido Zuccon; Bevan Koopman; | www | 2021-06-25 |
| 368 | Multilingual COVID-QA: Learning Towards Global Information Sharing Via Web Question Answering in Multiple Languages IF:2 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 |
| 369 | Multimodal Few-Shot Learning with Frozen Language Models IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). |
MARIA TSIMPOUKELLI et. al. | arxiv-cs.CV | 2021-06-25 |
| 370 | A Picture May Be Worth A Hundred Words for Visual Question Answering IF:2 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 |
| 371 | Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering IF:2 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 |
| 372 | Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this, we propose a fully Transformer visual embedding for VLP to better learn visual relation and further promote inter-modal alignment. |
HONGWEI XUE et. al. | arxiv-cs.CV | 2021-06-25 |
| 373 | Information Extraction From Co-Occurring Similar Entities IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. |
Nicolas Heist; Heiko Paulheim; | www | 2021-06-25 |
| 374 | Extracting Contextualized Quantity Facts from Web Tables IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enable such queries over web contents, this paper develops a novel method for automatically extracting quantity facts from ad-hoc web tables. |
Vinh Thinh Ho; Koninika Pal; Simon Razniewski; Klaus Berberich; Gerhard Weikum; | www | 2021-06-25 |
| 375 | Generating Accurate Caption Units for Figure Captioning IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We formulate the caption unit generation problem as a controlled captioning problem. Given a caption unit type as a control signal, a model generates an accurate caption unit of that type. |
XIN QIAN et. al. | www | 2021-06-25 |
| 376 | Taxonomy-aware Learning for Few-Shot Event Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with the low-resources problem, we investigate few-shot event detection in this paper and propose TaLeM, a novel taxonomy-aware learning model, consisting of two components, i.e., the taxonomy-aware self-supervised learning framework (TaSeLF) and the taxonomy-aware prototypical networks (TaPN). |
Jianming Zheng; Fei Cai; Wanyu Chen; Wengqiang Lei; Honghui Chen; | www | 2021-06-25 |
| 377 | 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 |
| 378 | AIT-QA: Question Answering Dataset Over Complex Tables in The Airline Industry IF:2 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 |
| 379 | VOGUE: Answer Verbalization Through Multi-Task Learning IF:2 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 |
| 380 | OKGIT: Open Knowledge Graph Link Prediction with Implicit Types IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address this problem in this work and propose OKGIT that improves OpenKG link prediction using novel type compatibility score and type regularization. |
Chandrahas; Partha Pratim Talukdar; | arxiv-cs.CL | 2021-06-24 |
| 381 | 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 |
| 382 | PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales IF:2 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 |
| 383 | ABCD: A Graph Framework to Convert Complex Sentences to A Covering Set of Simple Sentences IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. |
Yanjun Gao; Ting-hao; Huang; Rebecca J. Passonneau; | arxiv-cs.CL | 2021-06-22 |
| 384 | Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation IF:2 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 |
| 385 | Fine-tune The Entire RAG Architecture (including DPR Retriever) for Question-Answering IF:2 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 |
| 386 | VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis IF:2 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 |
| 387 | Weakly Supervised Pre-Training for Multi-Hop Retriever IF:2 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 |
| 388 | SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering Over Knowledge Graphs IF:2 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 |
| 389 | DocNLI: A Large-scale Dataset for Document-level Natural Language Inference IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents DocNLI — a newly-constructed large-scale dataset for document-level NLI. |
Wenpeng Yin; Dragomir Radev; Caiming Xiong; | arxiv-cs.CL | 2021-06-17 |
| 390 | Modeling Worlds in Text IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. |
Prithviraj Ammanabrolu; Mark O. Riedl; | arxiv-cs.CL | 2021-06-17 |
| 391 | Disentangling Online Chats with DAG-Structured LSTMs IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we apply DAG-LSTMs to the conversation disentanglement task. |
Duccio Pappadopulo; Lisa Bauer; Marco Farina; Ozan İrsoy; Mohit Bansal; | arxiv-cs.CL | 2021-06-16 |
| 392 | An Intelligent Question Answering System Based on Power Knowledge Graph IF:2 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 |
| 393 | Probing Image-Language Transformers for Verb Understanding IF:2 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 |
| 394 | How Modular Should Neural Module Networks Be for Systematic Generalization? IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate that the stage and the degree at which modularity is defined has large influence on systematic generalization. |
Vanessa D’Amario; Tomotake Sasaki; Xavier Boix; | arxiv-cs.LG | 2021-06-15 |
| 395 | Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering IF:2 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 |
| 396 | Question Answering Infused Pre-training of General-Purpose Contextualized Representations IF:2 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 |
| 397 | Exploiting Sentence-Level Representations for Passage Ranking IF:2 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 |
| 398 | Memory-efficient Transformers Via Top-$k$ Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a simple yet highly accurate approximation for vanilla attention. |
Ankit Gupta; Guy Dar; Shaya Goodman; David Ciprut; Jonathan Berant; | arxiv-cs.CL | 2021-06-12 |
| 399 | Roses Are Red, Violets Are Blue… But Should VQA Expect Them To? IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we claim that the standard evaluation metric, which consists in measuring the overall in-domain accuracy, is misleading. |
Corentin Kervadec; Grigory Antipov; Moez Baccouche; Christian Wolf; | cvpr | 2021-06-11 |
| 400 | Less Is More: ClipBERT for Video-and-Language Learning Via Sparse Sampling IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To provide a remedy to this dilemma, we propose a generic framework CLIPBERT that enables affordable end-to-end learning for video-and-language tasks, by employing sparse sampling, where only a single or a few sparsely sampled short clips from a video are used at each training step. |
JIE LEI et. al. | cvpr | 2021-06-11 |
| 401 | Improving Pretrained Cross-Lingual Language Models Via Self-Labeled Word Alignment IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce denoising word alignment as a new cross-lingual pre-training task. |
ZEWEN CHI et. al. | arxiv-cs.CL | 2021-06-11 |
| 402 | Domain-Robust VQA With Diverse Datasets and Methods But No Target Labels IF:2 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 |
| 403 | Vx2Text: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Vx2Text, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. |
XUDONG LIN et. al. | cvpr | 2021-06-11 |
| 404 | Look Before You Speak: Visually Contextualized Utterances IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide a solution in the form of a new visually conditioned Future Utterance Prediction task. |
Paul Hongsuck Seo; Arsha Nagrani; Cordelia Schmid; | cvpr | 2021-06-11 |
| 405 | Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We take a step further in pushing the limits of vision-and-language pre-training data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [Sharma et al. 2018] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training. |
Soravit Changpinyo; Piyush Sharma; Nan Ding; Radu Soricut; | cvpr | 2021-06-11 |
| 406 | Predicting Human Scanpaths in Visual Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new deep reinforcement learning method to predict scanpaths leading to different performances in visual question answering. |
Xianyu Chen; Ming Jiang; Qi Zhao; | cvpr | 2021-06-11 |
| 407 | UC2: Universal Cross-Lingual Cross-Modal Vision-and-Language Pre-Training IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To generalize this success to non-English languages, we introduce UC^2, the first machine translation-augmented framework for cross-lingual cross-modal representation learning. |
MINGYANG ZHOU et. al. | cvpr | 2021-06-11 |
| 408 | PQA: Perceptual Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we rejuvenate the study of perceptual organization, by advocating two positional changes: (i) we examine purposefully generated synthetic data, instead of complex real imagery, and (ii) we ask machines to synthesize novel perceptually-valid patterns, instead of explaining existing data. |
Yonggang Qi; Kai Zhang; Aneeshan Sain; Yi-Zhe Song; | cvpr | 2021-06-11 |
| 409 | TAP: Text-Aware Pre-Training for Text-VQA and Text-Caption IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Text-Aware Pre-training (TAP) for Text-VQA and Text-Caption tasks. |
ZHENGYUAN YANG et. al. | cvpr | 2021-06-11 |
| 410 | NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions IF:2 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 |
| 411 | Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing IF:2 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 |
| 412 | AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning IF:2 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 |
| 413 | Bridge To Answer: Structure-Aware Graph Interaction Network for Video Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel method, termed Bridge to Answer, to infer correct answers for questions about a given video by leveraging adequate graph interactions of heterogeneous crossmodal graphs. |
Jungin Park; Jiyoung Lee; Kwanghoon Sohn; | cvpr | 2021-06-11 |
| 414 | RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual Words IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To prove the generality of our proposals, we apply the two modules to the vanilla transformer model to build our Relationship-Sensitive Transformer (RSTNet) for image captioning task. |
XUYING ZHANG et. al. | cvpr | 2021-06-11 |
| 415 | How Transferable Are Reasoning Patterns in VQA? IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that uncertainty in vision is a dominating factor preventing the successful learning of reasoning in vision and language problems. |
CORENTIN KERVADEC et. al. | cvpr | 2021-06-11 |
| 416 | Found A Reason for Me? Weakly-supervised Grounded Visual Question Answering Using Capsules Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on a more relaxed setting: the grounding of relevant visual entities in a weakly supervised manner by training on the VQA task alone. |
AISHA UROOJ et. al. | cvpr | 2021-06-11 |
| 417 | TellMeWhy: A Dataset for Answering Why-Questions in Narratives IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce TellMeWhy, a new crowd-sourced dataset that consists of more than 30k questions and free-form answers concerning why characters in short narratives perform the actions described. |
Yash Kumar Lal; Nathanael Chambers; Raymond Mooney; Niranjan Balasubramanian; | arxiv-cs.CL | 2021-06-10 |
| 418 | NAAQA: A Neural Architecture for Acoustic Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, based on the previously introduced CLEAR dataset, we propose a new benchmark for AQA that emphasizes the specific challenges of acoustic inputs, e.g. variable duration scenes. |
Jerome Abdelnour; Jean Rouat; Giampiero Salvi; | arxiv-cs.CL | 2021-06-10 |
| 419 | End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering IF:2 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 Sachan; Siva Reddy; William Hamilton; Chris Dyer; Dani Yogatama; | arxiv-cs.CL | 2021-06-09 |
| 420 | DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an entity summary description. |
Weijia Shi; Mandar Joshi; Luke Zettlemoyer; | arxiv-cs.CL | 2021-06-09 |
| 421 | What Would A Teacher Do? Predicting Future Talk Moves IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a new task, called future talk move prediction (FTMP): it consists of predicting the next talk move — an utterance strategy from APT — given a conversation history with its corresponding talk moves. |
Ananya Ganesh; Martha Palmer; Katharina Kann; | arxiv-cs.CL | 2021-06-09 |
| 422 | CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpus as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question. In addition, we introduce two new open-domain benchmarks, E2E_WTQ and E2E_GNQ, consisting of 2,005 natural language questions over 76,242 tables. |
Feifei Pan; Mustafa Canim; Michael Glass; Alfio Gliozzo; Peter Fox; | arxiv-cs.CL | 2021-06-08 |
| 423 | Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using these augmentations, we propose a new robustness measure, Robustness to Augmented Data (RAD), which measures the consistency of model predictions between original and augmented examples. |
Daniel Rosenberg; Itai Gat; Amir Feder; Roi Reichart; | arxiv-cs.CV | 2021-06-08 |
| 424 | Check It Again: Progressive Visual Question Answering Via Visual Entailment IF:2 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; Mingyu Zheng; Peng Fu; Weiping Wang; | arxiv-cs.CV | 2021-06-08 |
| 425 | VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To facilitate the evaluation of such systems, we introduce Video-And-Language Understanding Evaluation (VALUE) benchmark, an assemblage of 11 VidL datasets over 3 popular tasks: (i) text-to-video retrieval; (ii) video question answering; and (iii) video captioning. |
LINJIE LI et. al. | arxiv-cs.CV | 2021-06-08 |
| 426 | XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop a new task-agnostic distillation framework XtremeDistilTransformers that leverages the advantage of task-specific methods for learning a small universal model that can be applied to arbitrary tasks and languages. |
Subhabrata Mukherjee; Ahmed Hassan Awadallah; Jianfeng Gao; | arxiv-cs.CL | 2021-06-08 |
| 427 | Comprehension Based Question Answering Using Bloom’s Taxonomy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Bloom’s Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. |
Pritish Sahu; Michael Cogswell; Sara Rutherford-Quach; Ajay Divakaran; | arxiv-cs.CL | 2021-06-08 |
| 428 | Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new challenge question answering dataset, Disfl-QA, a derivative of SQuAD, where humans introduce contextual disfluencies in previously fluent questions. |
Aditya Gupta; Jiacheng Xu; Shyam Upadhyay; Diyi Yang; Manaal Faruqui; | arxiv-cs.CL | 2021-06-07 |
| 429 | Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work provides a comprehensive and quantitative analysis about the difficulty of Book QA: (1) We benchmark the research on the NarrativeQA dataset with extensive experiments with cutting-edge ODQA techniques. |
XIANGYANG MOU et. al. | arxiv-cs.CL | 2021-06-07 |
| 430 | BERTnesia: Investigating The Capture and Forgetting of Knowledge in BERT IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we probe BERT specifically to understand and measure the relational knowledge it captures in its parametric memory. |
Jonas Wallat; Jaspreet Singh; Avishek Anand; | arxiv-cs.CL | 2021-06-05 |
| 431 | MERLOT: Multimodal Neural Script Knowledge Models IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce MERLOT, a model that learns multimodal script knowledge by watching millions of YouTube videos with transcribed speech — in an entirely label-free, self-supervised manner. |
ROWAN ZELLERS et. al. | arxiv-cs.CV | 2021-06-04 |
| 432 | Prediction or Comparison: Toward Interpretable Qualitative Reasoning IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we categorize qualitative reasoning tasks into two types: prediction and comparison. |
Mucheng Ren; Heyan Huang; Yang Gao; | arxiv-cs.CL | 2021-06-04 |
| 433 | Human-Adversarial Visual Question Answering IF:2 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. | arxiv-cs.CV | 2021-06-04 |
| 434 | Syntax-augmented Multilingual BERT for Cross-lingual Transfer IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work shows that explicitly providing language syntax and training mBERT using an auxiliary objective to encode the universal dependency tree structure helps cross-lingual transfer. |
Wasi Uddin Ahmad; Haoran Li; Kai-Wei Chang; Yashar Mehdad; | arxiv-cs.CL | 2021-06-03 |
| 435 | Self-supervised Dialogue Learning for Spoken Conversational Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we introduce a self-supervised learning approach, including incoherence discrimination, insertion detection, and question prediction, to explicitly capture the coreference resolution and dialogue coherence among spoken documents. |
Nuo Chen; Chenyu You; Yuexian Zou; | arxiv-cs.CL | 2021-06-03 |
| 436 | Grounding Complex Navigational Instructions Using Scene Graphs IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We adapt the CLEVR visual question answering dataset to generate complex natural language navigation instructions and accompanying scene graphs, yielding an environment-agnostic supervised dataset. |
Michiel de Jong; Satyapriya Krishna; Anuva Agarwal; | arxiv-cs.LG | 2021-06-03 |
| 437 | Answer Generation for Retrieval-based Question Answering Systems IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to generate answers from a set of AS2 top candidates. |
Chao-Chun Hsu; Eric Lind; Luca Soldaini; Alessandro Moschitti; | arxiv-cs.CL | 2021-06-02 |
| 438 | On The Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We use this pruning technique in conjunction with quantizing the attention values to only a 3-bit format, without retraining, resulting in only a 0.8% accuracy reduction on question answering with fine-tuned RoBERTa. |
TIANCHU JI et. al. | arxiv-cs.CL | 2021-06-02 |
| 439 | Ember: No-Code Context Enrichment Via Similarity-Based Keyless Joins IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response, we propose Ember, a system that abstracts and automates keyless joins to generalize context enrichment. |
Sahaana Suri; Ihab F. Ilyas; Christopher Ré; Theodoros Rekatsinas; | arxiv-cs.DB | 2021-06-02 |
| 440 | Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a new language understanding task, Linguistic Ethical Interventions (LEI), where the goal is to amend a question-answering (QA) model’s unethical behavior by communicating context-specific principles of ethics and equity to it. |
Jieyu Zhao; Daniel Khashabi; Tushar Khot; Ashish Sabharwal; Kai-Wei Chang; | arxiv-cs.CL | 2021-06-02 |
| 441 | Knowing More About Questions Can Help: Improving Calibration in Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study calibration in question answering, estimating whether model correctly predicts answer for each question. |
Shujian Zhang; Chengyue Gong; Eunsol Choi; | arxiv-cs.CL | 2021-06-02 |
| 442 | Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document, which we call directed sentiment extraction. From a million-scale news corpus, we construct a dataset of news sentences where sentiment relations of political entities are manually annotated. |
Kunwoo Park; Zhufeng Pan; Jungseock Joo; | arxiv-cs.CL | 2021-06-02 |
| 443 | Multilingual Medical Question Answering and Information Retrieval for Rural Health Intelligence Access IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe an approach leveraging the phenomenal progress in Machine Learning and NLP (Natural Language Processing) techniques to design a model that is low-resource, multilingual, and a preliminary first-point-of-contact medical assistant. |
Vishal Vinod; Susmit Agrawal; Vipul Gaurav; Pallavi R; Savita Choudhary; | arxiv-cs.CL | 2021-06-02 |
| 444 | Minimax and Neyman-Pearson Meta-Learning for Outlier Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In light of this, we propose a new adaptive optimiser solving for a local approximation to their Nash equilibrium. |
Edoardo Maria Ponti; Rahul Aralikatte; Disha Shrivastava; Siva Reddy; Anders Søgaard; | arxiv-cs.CL | 2021-06-02 |
| 445 | A Coarse to Fine Question Answering System Based on Reinforcement Learning IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a coarse to fine question answering (CFQA) system based on reinforcement learning which can efficiently processes documents with different lengths by choosing appropriate actions. |
Yu Wang; Hongxia Jin; | arxiv-cs.CL | 2021-06-01 |
| 446 | DoT: An Efficient Double Transformer for NLP Tasks with Tables IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve efficiency while maintaining a high accuracy, we propose a new architecture, DoT, a double transformer model, that decomposes the problem into two sub-tasks: A shallow pruning transformer that selects the top-K tokens, followed by a deep task-specific transformer that takes as input those K tokens. |
Syrine Krichene; Thomas Müller; Julian Martin Eisenschlos; | arxiv-cs.CL | 2021-06-01 |
| 447 | Conversational Question Answering: A Survey IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This survey, therefore, is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers from 2016-2021. |
Munazza Zaib; Wei Emma Zhang; Quan Z. Sheng; Adnan Mahmood; Yang Zhang; | arxiv-cs.CL | 2021-06-01 |
| 448 | ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this gap, we design annotation protocols motivated by an issues–viewpoints–assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. |
ALEXANDER R. FABBRI et. al. | arxiv-cs.CL | 2021-06-01 |
| 449 | Question-aware Transformer Models for Consumer Health Question Summarization IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the task of abstractive summarization for real-world consumer health questions. |
Shweta Yadav; Deepak Gupta; Asma Ben Abacha; Dina Demner-Fushman; | arxiv-cs.CL | 2021-06-01 |
| 450 | Parameter-Efficient Neural Question Answering Models Via Graph-Enriched Document Representations IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that a GCN enriched document representation greatly improves the results seen in HotPotQA, even when using a trivial topology. |
Louis Castricato; Stephen Fitz; Won Young Shin; | arxiv-cs.CL | 2021-06-01 |
| 451 | Bangla Natural Language Processing: A Comprehensive Review of Classical, Machine Learning, and Deep Learning Based Methods IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. |
OVISHAKE SEN et. al. | arxiv-cs.CL | 2021-05-31 |
| 452 | Corpus-Based Paraphrase Detection Experiments and Review IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we give a performance overview of various types of corpus-based models, especially deep learning (DL) models, with the task of paraphrase detection. |
Tedo Vrbanec; Ana Mestrovic; | arxiv-cs.CL | 2021-05-31 |
| 453 | End-to-End Multihop Retrieval for Compositional Question Answering Over Long Documents IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-hop retrieval method, DocHopper, to answer compositional questions over long documents. |
Haitian Sun; William W. Cohen; Ruslan Salakhutdinov; | arxiv-cs.CL | 2021-05-31 |
| 454 | GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. |
JIAQI CHEN et. al. | arxiv-cs.AI | 2021-05-30 |
| 455 | Is Sluice Resolution Really Just Question Answering? IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Sluice resolution is a problem where a system needs to output the corresponding antecedents of wh-ellipses. The antecedents are elided contents behind the wh-words but are … |
Peratham Wiriyathammabhum; | arxiv-cs.CL | 2021-05-29 |
| 456 | Inside ASCENT: Exploring A Deep Commonsense Knowledge Base and Its Usage in Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. |
Tuan-Phong Nguyen; Simon Razniewski; Gerhard Weikum; | arxiv-cs.AI | 2021-05-28 |
| 457 | Feature Extraction and Evaluation for BioMedical Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present our work on the BioASQ pipeline. |
Ankit Shah; Srishti Singh; Shih-Yen Tao; | arxiv-cs.CL | 2021-05-28 |
| 458 | Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering The 2002 Gujarat Violence IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We combine corpus-level evaluation requirements with a real-world, social science setting and introduce the IndiaPoliceEvents corpus–all 21,391 sentences from 1,257 English-language Times of India articles about events in the state of Gujarat during March 2002. |
Andrew Halterman; Katherine A. Keith; Sheikh Muhammad Sarwar; Brendan O’Connor; | arxiv-cs.CL | 2021-05-27 |
| 459 | Improve Query Focused Abstractive Summarization By Incorporating Answer Relevance IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model, to generate coherent and answer-related summaries. |
Dan Su; Tiezheng Yu; Pascale Fung; | arxiv-cs.CL | 2021-05-27 |
| 460 | Multi-turn Dialog System on Single-turn Data in Medical Domain IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This research is focused on the multi-turn dialog system trained on the multi-turn dialog data. |
NAZIB SORATHIYA et. al. | arxiv-cs.CL | 2021-05-26 |
| 461 | Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details |