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Paper Digest: Recent Papers on Question Answering

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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.

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TABLE 1: Paper Digest: Recent Papers on Question Answering

Title Author(s) Highlight Category Date
1 Big Bird: Transformers for Longer Sequences MANZIL ZAHEER et. al. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. CS.arxiv 2020-07-28
2 A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges BIN FU et. al. After describing the methods of these branches, we analyze directions for future research and introduce the models proposed by the Alime team. CS.arxiv 2020-07-26
3 REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question Answering SIWEN LUO et. al. In this paper, we propose a deep reasoning VQA model with explicit visual structure-aware textual information, and it works well in capturing step-by-step reasoning process and detecting a complex object-relationship in photo-realistic images. CS.arxiv 2020-07-26
4 User-Inspired Posterior Network for Recommendation Reason Generation HAOLAN ZHAN et. al. Therefore, in this paper, we consider generating the recommendation reason by taking into account not only the product attributes but also the customer-generated product QA discussions. CS.sigir 2020-07-25
5 Incorporating User Feedback in Conversational Question Answering over Heterogeneous Web Sources MAGDALENA KAISER This PhD thesis will explore conversational question answering with a special emphasis on incorporating user feedback. CS.sigir 2020-07-25
6 DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding PING NIE et. al. To address the efficiency problem, we propose DC-BERT, a decoupled contextual encoding framework that has dual BERT models: an online BERT which encodes the question only once, and an offline BERT which pre-encodes all the documents and caches their encodings. CS.sigir 2020-07-25
7 Conversational Question Answering over Passages by Leveraging Word Proximity Networks MAGDALENA KAISER et. al. In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns. CS.sigir 2020-07-25
8 The Cortical Activity of Graded Relevance ZUZANA PINKOSOVA et. al. In this paper, we aim to investigate the brain activity associated with relevance when it is treated as a graded concept. CS.sigir 2020-07-25
9 Open-Retrieval Conversational Question Answering CHEN QU et. al. To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. CS.sigir 2020-07-25
10 Multi-Document Answer Generation for Non-Factoid Questions VALERIIA BARANOVA-BOLOTOVA The current research will be devoted to the challenging and under-investigated task of multi-source answer generation for complex non-factoid questions. CS.sigir 2020-07-25
11 Macaw: An Extensible Conversational Information Seeking Platform HAMED ZAMANI et. al. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. CS.sigir 2020-07-25
12 Residual-Duet Network with Tree Dependency Representation for Chinese Question-Answering Sentiment Analysis GUANGYI HU et. al. In this work, we propose a Residual-Duet Network leveraging textual and tree dependency information for Chinese question-answering sentiment analysis. CS.sigir 2020-07-25
13 Efficient Document Re-Ranking for Transformers by Precomputing Term Representations SEAN MACAVANEY et. al. Our proposed approach, called PreTTR (Precomputing Transformer Term Representations), considerably reduces the query-time latency of deep transformer networks (up to a 42x speedup on web document ranking) making these networks more practical to use in a real-time ranking scenario. CS.sigir 2020-07-25
14 Deep Neural Matching Models for Graph Retrieval KUNAL GOYAL et. al. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. CS.sigir 2020-07-25
15 Large-scale Multi-modal Search and QA at Alibaba RONG JIN In this talk, we will present ongoing efforts of Alibaba on large-scale multi-modal search ad question-answering, including image search, video search, cross lingual retrieval, and QA based on machine reading and comprehension (MRC). CS.sigir 2020-07-25
16 Having Your Cake and Eating it Too: Training Neural Retrieval for Language Inference without Losing Lexical Match VIKAS YADAV et. al. To mitigate this limitation, we introduce a supervised RoBERTa QA method that is trained to mimic the behavior of BM25 and the soft-matching idea behind embedding-based alignment methods. CS.sigir 2020-07-25
17 Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization YANG DENG et. al. In this paper, we propose a unified model to bridge hierarchical and sequential context modeling for question-driven extractive answer summarization. CS.sigir 2020-07-25
18 Question Answering over Curated and Open Web Sources RISHIRAJ SAHA ROY et. al. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. CS.sigir 2020-07-25
19 Match²: A Matching over Matching Model for Similar Question Identification ZIZHEN WANG et. al. In this work, we propose atwo-side usage, which leverages the answer as a bridge of the two questions. CS.sigir 2020-07-25
20 Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation JIANMING ZHENG et. al. In this paper, we propose a unified fine-tuning architecture, incorporated with scenario knowledge for event representation, i.e., UniFA-S, which mainly consists of a unified fine-tuning architecture (UniFA) and a scenario-level variational auto-encoder (S-VAE). CS.sigir 2020-07-25
21 Humor Detection in Product Question Answering Systems YFTAH ZISER et. al. In this study we present a deep-learning framework for detecting humorous questions in PQA systems. CS.sigir 2020-07-25
22 Investigating Reading Behavior in Fine-grained Relevance Judgment ZHIJING WU et. al. To shed light on this research question, we investigate how users allocate their attention to passages of a document during the relevance judgment process. CS.sigir 2020-07-25
23 Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling WENXUAN ZHANG et. al. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking. CS.sigir 2020-07-25
24 QuAChIE: Question Answering based Chinese Information Extraction System DONGYU RU et. al. In this paper, we present the design of QuAChIE, a Question Answering based Chinese Information Extraction system. For the training and evaluation of each model in the system, we build a large-scale information extraction dataset using Wikidata and Wikipedia pages by distant supervision. CS.sigir 2020-07-25
25 Reranking for Efficient Transformer-based Answer Selection YOSHITOMO MATSUBARA et. al. In this paper, we show that standard and efficient neural rerankers can be used to reduce the amount of sentence candidates fed to Transformer models without hurting Accuracy, thus improving efficiency up to four times. CS.sigir 2020-07-25
26 A Survey on Graph Neural Networks for Knowledge Graph Completion SIDDHANT ARORA In this survey, we understand the various strengths and weaknesses of the proposed methodology and try to find new exciting research problems in this area that require further investigation. CS.arxiv 2020-07-24
27 Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification NICOLE PEINELT et. al. We therefore propose two ways of merging topics with word embeddings (early vs. late fusion) in a new neural architecture for question paraphrase identification. CS.arxiv 2020-07-22
28 ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation DONGLING XIAO et. al. To address this issue, we propose an enhanced multi-flow sequence to sequence pre-training and fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method. CS.ijcai 2020-07-21
29 Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering ZIHAO ZHU et. al. In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features. CS.ijcai 2020-07-21
30 Dynamic Language Binding in Relational Visual Reasoning THAO MINH LE et. al. We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering. CS.ijcai 2020-07-21
31 Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base YONGRUI CHEN et. al. In this paper, we propose a new formal query building approach that consists of two stages. CS.ijcai 2020-07-21
32 Video Question Answering on Screencast Tutorials WENTIAN ZHAO et. al. This paper presents a new video question answering task on screencast tutorials. We introduce a dataset including question, answer and context triples from the tutorial videos for a software. CS.ijcai 2020-07-21
33 EndCold: An End-to-End Framework for Cold Question Routing in Community Question Answering Services JIANKAI SUN et. al. Therefore, we propose to address the challenge of leveraging heterogeneous graph and textual information for cold question routing by designing an end-to-end framework that jointly learns CQA node embeddings and finds best answerers for cold questions. CS.ijcai 2020-07-21
34 Learning to Discretely Compose Reasoning Module Networks for Video Captioning GANCHAO TAN et. al. In this paper, we propose a novel visual reasoning approach for video captioning, named Reasoning Module Networks (RMN), to equip the existing encoder-decoder framework with the above reasoning capacity. CS.ijcai 2020-07-21
35 Towards Quantum Belief Propagation for LDPC Decoding in Wireless Networks SRIKAR KASI et. al. Our further studies in this work present future cost, throughput, and QA hardware trend considerations. CS.arxiv 2020-07-21
36 Feature Augmented Memory with Global Attention Network for VideoQA JIAYIN CAI et. al. To tackle these problems, we propose a novel VideoQA framework which progressively refines the representations of videos and questions from fine to coarse grain in a sequence-sensitive manner. CS.ijcai 2020-07-21
37 Transformers as Soft Reasoners over Language PETER CLARK et. al. This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences, thus bypassing a formal representation. CS.ijcai 2020-07-21
38 AILA: A Question Answering System in the Legal Domain WEIYI HUANG et. al. In this paper, we develop an Artificial Intelligence Law Assistant (AILA) for question answering in the domain of Chinese laws. CS.ijcai 2020-07-21
39 Neural Abstractive Summarization with Structural Attention TANYA CHOWDHURY et. al. In this work, we present a hierarchical encoder based on structural attention to model such inter-sentence and inter-document dependencies. CS.ijcai 2020-07-21
40 Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering TAO SHEN et. al. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. CS.ijcai 2020-07-21
41 Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction TIANYANG ZHAO et. al. In this paper, we improve the existing MRC-based entity-relation extraction model through diverse question answering. CS.ijcai 2020-07-21
42 Two-Phase Hypergraph Based Reasoning with Dynamic Relations for Multi-Hop KBQA JIALE HAN et. al. Inspired by human’s hop-by-hop reasoning, we propose an interpretable KBQA model based on DHGCN, namely two-phase hypergraph based reasoning with dynamic relations, which explicitly updates relation information and dynamically pays attention to different relations at different hops. CS.ijcai 2020-07-21
43 Overcoming Language Priors with Self-supervised Learning for Visual Question Answering XI ZHU et. al. In this paper, we introduce a self-supervised learning framework to solve this problem. Concretely, we first automatically generate labeled data to balance the biased data, and then propose a self-supervised auxiliary task to utilize the balanced data to assist the VQA model to overcome language priors. CS.ijcai 2020-07-21
44 Generating Reasonable Legal Text through the Combination of Language Modeling and Question Answering WEIJING HUANG et. al. To generate reasonable legal documents, we propose a novel method CoLMQA, which (1) combines Language Modeling and Question Answering, (2) generates text with slots by Language Modeling, and (3) fills the slots by our proposed Question Answering method named Transformer-based Key-Value Memory Networks. CS.ijcai 2020-07-21
45 LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning JIAN LIU et. al. We build a comprehensive dataset, named LogiQA, which is sourced from expert-written questions for testing human Logical reasoning. CS.ijcai 2020-07-21
46 Sybil-proof Answer Querying Mechanism YAO ZHANG et. al. We study a question answering problem on a social network, where a requester is seeking an answer from the agents on the network. CS.ijcai 2020-07-21
47 Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning YUNCHENG HUA et. al. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system’s performance with respect to the produced answers. CS.ijcai 2020-07-21
48 Distributed Memory based Self-Supervised Differentiable Neural Computer TAEWON PARK et. al. To address those issues, we propose a novel distributed memory-based self-supervised DNC architecture for enhanced memory augmented neural network performance. CS.arxiv 2020-07-21
49 Frustratingly Hard Evidence Retrieval for QA Over Books XIANGYANG MOU et. al. We formulate BookQA as an open-domain QA task given its similar dependency on evidence retrieval. CS.arxiv 2020-07-20
50 Multimodal Dialogue State Tracking By QA Approach with Data Augmentation XIANGYANG MOU et. al. This paper interprets the AVSD task from an open-domain Question Answering (QA) point of view and proposes a multimodal open-domain QA system to deal with the problem. CS.arxiv 2020-07-20
51 Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering RUIXUE TANG et. al. In this paper, instead of directly manipulating images and questions, we use generated adversarial examples for both images and questions as the augmented data. CS.arxiv 2020-07-19
52 Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions NOA GARCIA et. al. Inspired by this behaviour, we design ROLL, a model for knowledge-based video story question answering that leverages three crucial aspects of movie understanding: dialog comprehension, scene reasoning, and storyline recalling. CS.arxiv 2020-07-17
53 Visual Relation Grounding in Videos JUNBIN XIAO et. al. In this paper, we explore a novel task named visual Relation Grounding in Videos (vRGV). CS.arxiv 2020-07-17
54 Template-Based Question Answering over Linked Geospatial Data DHARMEN PUNJANI et. al. In this paper, we go beyond these approaches to offer a question-answering engine for natural language questions on top of linked geospatial data sources. CS.arxiv 2020-07-14
55 XAlgo: Explaining the Internal States of Algorithms via Question Answering JUAN REBANAL et. al. We contribute a formal model that first classifies the type of question based on a taxonomy, and generates an answer based on a set of rules that extract information from representations of an algorithm’s internal states, e.g., the pseudocode. CS.arxiv 2020-07-14
56 Using Holographically Compressed Embeddings in Question Answering SALVADOR E. BARBOSA This research employs holographic compression of pre-trained embeddings, to represent a token, its part-of-speech, and named entity type, in the same dimension as representing only the token. CS.arxiv 2020-07-14
57 Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder GOUTHAMAN KV et. al. In this work, we propose a novel model-agnostic question encoder, Visually-Grounded Question Encoder (VGQE), for VQA that reduces this effect. CS.arxiv 2020-07-13
58 The Effect of Natural Distribution Shift on Question Answering Models JOHN MILLER et. al. Taken together, our results confirmthe surprising resilience of the holdout methodand emphasize the need to move towards evalua-tion metrics that incorporate robustness to naturaldistribution shifts. CS.icml 2020-07-11
59 Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning" SAEED AMIZADEH et. al. To address this, we propose (1) a framework to isolate and evaluate the reasoning aspect of VQA separately from its perception, and (2) a novel top-down calibration technique that allows the model to answer reasoning questions even with imperfect perception. CS.icml 2020-07-11
60 Probing Emergent Semantics in Predictive Agents via Question Answering ABHISHEK DAS et. al. We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modelling – action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019). CS.icml 2020-07-11
61 Description Based Text Classification with Reinforcement Learning WEI WU et. al. Inspired by the current trend of formalizing NLP problems as question answering tasks, we propose a new framework for text classification, in which each category label is associated with a category description. CS.icml 2020-07-11
62 Applying recent advances in Visual Question Answering to Record Linkage MARKO SMILEVSKI This field has not been explored in research and we propose two solutions based on Deep Learning architectures that are inspired by recent work in Visual Question Answering. CS.arxiv 2020-07-11
63 Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning QING LI et. al. In this paper, we address these issues and close the loop of neural-symbolic learning by (1) introducing the grammar model as a symbolic prior to bridge neural perception and symbolic reasoning, and (2) proposing a novel back-search algorithm which mimics the top-down human-like learning procedure to propagate the error through the symbolic reasoning module efficiently. CS.icml 2020-07-11
64 Multigrid Neural Memory TRI HUYNH et. al. We introduce a radical new approach to endowing neural networks with access to long-term and large-scale memory. CS.icml 2020-07-11
65 Retrieval Augmented Language Model Pre-Training KELVIN GUU et. al. To capture knowledge in a more modular and interpretable way, we augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. CS.icml 2020-07-11
66 Self-Attentive Associative Memory HUNG LE et. al. In this paper, we propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory). CS.icml 2020-07-11
67 Image Captioning with Compositional Neural Module Networks JUNJIAO TIAN et. al. Inspired by the idea of the compositional neural module networks in the visual question answering task, we introduce a hierarchical framework for image captioning that explores both compositionality and sequentiality of natural language. CS.arxiv 2020-07-10
68 Not Your Grandfathers Test Set: Reducing Labeling Effort for Testing BEGUM TASKAZAN et. al. This paper proposes a simple but effective technique that drastically reduces the effort needed to construct and maintain a high-quality test set (reducing labeling effort by 80-100% across a range of practical scenarios). CS.arxiv 2020-07-10
69 What Can We Learn From Almost a Decade of Food Tweets UGA SPROĢIS et. al. We present the Latvian Twitter Eater Corpus – a set of tweets in the narrow domain related to food, drinks, eating and drinking. CS.arxiv 2020-07-10
70 Advances of Transformer-Based Models for News Headline Generation ALEXEY BUKHTIYAROV et. al. In this paper, we fine-tune two pretrained Transformer-based models (mBART and BertSumAbs) for that task and achieve new state-of-the-art results on the RIA and Lenta datasets of Russian news. CS.arxiv 2020-07-09
71 IQ-VQA: Intelligent Visual Question Answering VATSAL GOEL et. al. To this end, we propose a model-independent cyclic framework which increases consistency and robustness of any VQA architecture. As a baseline for future works on consistency, we provide a new human annotated VQA-Implications dataset. CS.arxiv 2020-07-08
72 Less is More: Rejecting Unreliable Reviews for Product Question Answering SHIWEI ZHANG et. al. In this paper, we focus on the issue of answerability and answer reliability for PQA using reviews. CS.arxiv 2020-07-08
73 KQA Pro: A Large Diagnostic Dataset for Complex Question Answering over Knowledge Base JIAXIN SHI et. al. To this end, we introduce KQA Pro, a large-scale dataset for Complex KBQA. CS.arxiv 2020-07-07
74 What Gives the Answer Away? Question Answering Bias Analysis on Video QA Datasets JIANING YANG et. al. In this paper, we analyze QA biases in popular video question answering datasets and discover pretrained language models can answer 37-48% questions correctly without using any multimodal context information, far exceeding the 20% random guess baseline for 5-choose-1 multiple-choice questions. CS.arxiv 2020-07-07
75 Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training YINGWEI PAN et. al. In this work, we present Auto-captions on GIF, which is a new large-scale pre-training dataset for generic video understanding. CS.arxiv 2020-07-05
76 Modality Shifting Attention Network for Multi-modal Video Question Answering JUNYEONG KIM et. al. This paper considers a network referred to as Modality Shifting Attention Network (MSAN) for Multimodal Video Question Answering (MVQA) task. CS.arxiv 2020-07-04
77 Text Data Augmentation: Towards better detection of spear-phishing emails MEHDI REGINA et. al. Motivated by a business application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic text augmentation framework combining different methods, utilizing BERT language model, multi-step back-translation and heuristics. CS.arxiv 2020-07-04
78 Visual Question Answering as a Multi-Task Problem AMELIA ELIZABETH POLLARD et. al. In this paper, we present the hypothesis that Visual Question Answering should be viewed as a multi-task problem, and provide evidence to support this hypothesis. CS.arxiv 2020-07-03
79 El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks MARIA KHVALCHIK et. al. In this work, we study the caveats of applying directly translated corpora for fine-tuning LMs for downstream natural language processing tasks and demonstrate that careful curation along with post-processing lead to improved performance and overall LMs robustness. CS.arxiv 2020-07-03
80 A Competence-aware Curriculum for Visual Concepts Learning via Question Answering QING LI et. al. To mimic this efficient learning ability, we propose a competence-aware curriculum for visual concept learning in a question-answering manner. CS.arxiv 2020-07-03
81 Improving Event Detection using Contextual Word and Sentence Embeddings MARIANO MAISONNAVE et. al. The main contribution of this paper is the design, implementation and evaluation of a recurrent neural network model for ED that combines several features. CS.arxiv 2020-07-02
82 Scene Graph Reasoning for Visual Question Answering MARCEL HILDEBRANDT et. al. We propose a novel method that approaches the task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene. CS.arxiv 2020-07-02
83 Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering GAUTIER IZACARD et. al. In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. CS.arxiv 2020-07-02
84 Project PIAF: Building a Native French Question-Answering Dataset RACHEL KERARON et. al. Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. CS.arxiv 2020-07-02
85 IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE LUXI XING et. al. This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation. CS.arxiv 2020-07-02
86 The Impact of Explanations on AI Competency Prediction in VQA KAMRAN ALIPOUR et. al. In this paper, we evaluate the impact of explanations on the user’s mental model of AI agent competency within the task of visual question answering (VQA). CS.arxiv 2020-07-02
87 DocVQA: A Dataset for VQA on Document Images MINESH MATHEW et. al. We present a new dataset for Visual Question Answering on document images called DocVQA. CS.arxiv 2020-07-01
88 COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation QINGYUN WANG et. al. We have developed a novel and comprehensive knowledge discovery framework, \textbf{COVID-KG} to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. CS.arxiv 2020-07-01
89 Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge PAT VERGA et. al. To address these problems, we develop a neural language model that includes an explicit interface between symbolically interpretable factual information and subsymbolic neural knowledge. CS.arxiv 2020-07-01
90 Relevance-guided Supervision for OpenQA with ColBERT OMAR KHATTAB et. al. We propose a weak supervision strategy that iteratively uses ColBERT to create its own training data. CS.arxiv 2020-07-01
91 Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering BEN BOGIN et. al. In this work, we propose a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser. CS.arxiv 2020-07-01
92 Transferability of Natural Language Inference to Biomedical Question Answering MINBYUL JEONG et. al. In this paper, we focus on facilitating the transferability by unifying the experimental setup from natural language inference (NLI) to biomedical QA. CS.arxiv 2020-07-01
93 Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering XINYAN ZHAO et. al. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CS.arxiv 2020-06-30
94 Ontology-guided Semantic Composition for Zero-Shot Learning JIAOYAN CHEN et. al. Ontology-guided Semantic Composition for Zero-Shot Learning CS.arxiv 2020-06-30
95 Answering Questions on COVID-19 in Real-Time JINHYUK LEE et. al. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. CS.arxiv 2020-06-29
96 Improving VQA and its Explanations \\ by Comparing Competing Explanations JIALIN WU et. al. To address this issue, we present a novel framework that uses explanations for competing answers to help VQA systems select the correct answer. CS.arxiv 2020-06-28
97 Graph Optimal Transport for Cross-Domain Alignment LIQUN CHEN et. al. We propose Graph Optimal Transport (GOT), a principled framework that germinates from recent advances in Optimal Transport (OT). CS.arxiv 2020-06-25
98 Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering CHIRANJIB SUR We defined two strategies: Self-Segregating Transformer (SST) and Coordinated-Segregating Transformer (CST) and used it to solve visual question answering application. CS.arxiv 2020-06-25
99 Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance GAGAN BANSAL et. al. Can we develop explanatory approaches that help humans decide whether and when to trust AI input? CS.arxiv 2020-06-25
100 AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types XIN LUNA DONG et. al. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. CS.arxiv 2020-06-24
101 NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature JENNIFER D’SOUZA et. al. In this article, we describe the outcomes of this pilot annotation phase. CS.arxiv 2020-06-23
102 ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion BINGNINGWANG et. al. This paper presents the ReCO, a human-curated ChineseReading Comprehension dataset on Opinion. CS.arxiv 2020-06-22
103 Match$^2$: A Matching over Matching Model for Similar Question Identification ZIZHEN WANG et. al. In this work, we propose a two-side usage, which leverages the answer as a bridge of the two questions. CS.arxiv 2020-06-21
104 Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings APOORV SAXENA et. al. We fill this gap in this paper and propose EmbedKGQA. EmbedKGQA is particularly effective in performing multi-hop KGQA over sparse KGs. CS.acl 2020-06-20
105 QuASE: Question-Answer Driven Sentence Encoding HANGFENG HE et. al. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? CS.acl 2020-06-20
106 Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering HAO CHENG et. al. We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings. CS.acl 2020-06-20
107 Logic-Guided Data Augmentation and Regularization for Consistent Question Answering AKARI ASAI et. al. This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models. CS.acl 2020-06-20
108 MLQA: Evaluating Cross-lingual Extractive Question Answering PATRICK LEWIS et. al. We present MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area. CS.acl 2020-06-20
109 Multimodal Neural Graph Memory Networks for Visual Question Answering MAHMOUD KHADEMI We introduce a new neural network architecture, Multimodal Neural Graph Memory Networks (MN-GMN), for visual question answering. CS.acl 2020-06-20
110 Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering CHANGMAO LI et. al. We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. CS.acl 2020-06-20
111 A negative case analysis of visual grounding methods for VQA ROBIK SHRESTHA et. al. However, we show that the performance improvements are not a result of improved visual grounding, but a regularization effect which prevents over-fitting to linguistic priors. CS.acl 2020-06-20
112 Temporal Common Sense Acquisition with Minimal Supervision BEN ZHOU et. al. This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense, extracted from a large corpus, to build TacoLM, a temporal common sense language model. CS.acl 2020-06-20
113 Selective Question Answering under Domain Shift AMITA KAMATH et. al. In this work, we propose the setting of selective question answering under domain shift, in which a QA model is tested on a mixture of in-domain and out-of-domain data, and must answer (i.e., not abstain on) as many questions as possible while maintaining high accuracy. CS.acl 2020-06-20
114 Aligned Dual Channel Graph Convolutional Network for Visual Question Answering QINGBAO HUANG et. al. To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph convolutional network (DC-GCN) for better combining visual and textual advantages. CS.acl 2020-06-20
115 Neuro-Symbolic Visual Reasoning: Disentangling Visual from Reasoning SAEED AMIZADEH et. al. To address this, we propose (1) a framework to isolate and evaluate the reasoning aspect of VQA separately from its perception, and (2) a novel top-down calibration technique that allows the model to answer reasoning questions even with imperfect perception. CS.arxiv 2020-06-20
116 Memory Transformer MIKHAIL S. BURTSEV et. al. In this work, we propose and study two extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, and (2) creating memory bottleneck for the global information. CS.arxiv 2020-06-20
117 Span Selection Pre-training for Question Answering MICHAEL GLASS et. al. In this paper we introduce a new pre-training task inspired by reading comprehension to better align the pre-training from memorization to understanding. CS.acl 2020-06-20
118 IQA: Interactive Query Construction in Semantic Question Answering Systems HAMID ZAFAR et. al. In this article, we aim to empower users in guiding SQA systems towards the intended semantic queries through interaction. CS.arxiv 2020-06-20
119 Learning to Identify Follow-Up Questions in Conversational Question Answering SOUVIK KUNDU et. al. In this paper, we introduce a new follow-up question identification task. CS.acl 2020-06-20
120 On the Cross-lingual Transferability of Monolingual Representations MIKEL ARTETXE et. al. More concretely, we first train a transformer-based masked language model on one language, and transfer it to a new language by learning a new embedding matrix with the same masked language modeling objective, freezing parameters of all other layers. CS.acl 2020-06-20
121 Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA HYOUNGHUN KIM et. al. In this paper, we propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions. CS.acl 2020-06-20
122 TVQA+: Spatio-Temporal Grounding for Video Question Answering JIE LEI et. al. We present the task of Spatio-Temporal Video Question Answering, which requires intelligent systems to simultaneously retrieve relevant moments and detect referenced visual concepts (people and objects) to answer natural language questions about videos. CS.acl 2020-06-20
123 Span-based Localizing Network for Natural Language Video Localization HAO ZHANG et. al. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. CS.acl 2020-06-20
124 Calibrating Structured Output Predictors for Natural Language Processing ABHYUDAY JAGANNATHA et. al. In this study, we propose a general calibration scheme for output entities of interest in neural network based structured prediction models. CS.acl 2020-06-20
125 TaPas: Weakly Supervised Table Parsing via Pre-training JONATHAN HERZIG et. al. In this paper, we present TaPas, an approach to question answering over tables without generating logical forms. CS.acl 2020-06-20
126 Controlled Crowdsourcing for High-Quality QA-SRL Annotation PAUL ROIT et. al. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. CS.acl 2020-06-20
127 Fluent Response Generation for Conversational Question Answering ASHUTOSH BAHETI et. al. In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. CS.acl 2020-06-20
128 A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation JAN DERIU et. al. In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. CS.acl 2020-06-20
129 Contextualized Sparse Representations for Real-Time Open-Domain Question Answering JINHYUK LEE et. al. In this paper, we aim to improve the quality of each phrase embedding by augmenting it with a contextualized sparse representation (Sparc). CS.acl 2020-06-20
130 DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering QINGQING CAO et. al. We introduce DeFormer, a decomposed transformer, which substitutes the full self-attention with question-wide and passage-wide self-attentions in the lower layers. CS.acl 2020-06-20
131 On the Importance of Diversity in Question Generation for QA MD ARAFAT SULTAN et. al. In this paper we ask: Is textual diversity in QG beneficial for downstream QA? CS.acl 2020-06-20
132 FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization ESIN DURMUS et. al. We tackle the problem of evaluating faithfulness of a generated summary given its source document. CS.acl 2020-06-20
133 Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension HONGYU GONG et. al. In this paper, we study machine reading comprehension (MRC) on long texts: where a model takes as inputs a lengthy document and a query, extracts a text span from the document as an answer. CS.acl 2020-06-20
134 DoQA – Accessing Domain-Specific FAQs via Conversational QA JON ANDER CAMPOS et. al. The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. CS.acl 2020-06-20
135 Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs DONG BOK LEE et. al. In this work, we propose a hierarchical conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QA pairs to ensure their consistency. CS.acl 2020-06-20
136 What Question Answering can Learn from Trivia Nerds JORDAN BOYD-GRABER et. al. We argue that creating a QA dataset-and the ubiquitous leaderboard that goes with it-closely resembles running a trivia tournament: you write questions, have agents (either humans or machines) answer the questions, and declare a winner. CS.acl 2020-06-20
137 The Cascade Transformer: an Application for Efficient Answer Sentence Selection LUCA SOLDAINI et. al. In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers. CS.acl 2020-06-20
138 Cross-Modality Relevance for Reasoning on Language and Vision CHEN ZHENG et. al. This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR). CS.acl 2020-06-20
139 The TechQA Dataset VITTORIO CASTELLI et. al. We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain. CS.acl 2020-06-20
140 Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering ALEXANDER FABBRI et. al. We propose an unsupervised approach to training QA models with generated pseudo-training data. CS.acl 2020-06-20
141 KLEJ: Comprehensive Benchmark for Polish Language Understanding PIOTR RYBAK et. al. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language understanding, accompanied by an online leaderboard. CS.acl 2020-06-20
142 Towards Conversational Recommendation over Multi-Type Dialogs ZEMING LIU et. al. We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user’s interests and feedback. CS.acl 2020-06-20
143 A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction YILIN NIU et. al. To address this problem, we present a Self-Training method (STM), which supervises the evidence extractor with auto-generated evidence labels in an iterative process. CS.acl 2020-06-20
144 Harvesting and Refining Question-Answer Pairs for Unsupervised QA ZHONGLI LI et. al. In this work, we introduce two approaches to improve unsupervised QA. CS.acl 2020-06-20
145 MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices ZHIQING SUN et. al. In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model. CS.acl 2020-06-20
146 Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples XIAOQING ZHENG et. al. In this study, we show that adversarial examples also exist in dependency parsing: we propose two approaches to study where and how parsers make mistakes by searching over perturbations to existing texts at sentence and phrase levels, and design algorithms to construct such examples in both of the black-box and white-box settings. CS.acl 2020-06-20
147 CorefQA: Coreference Resolution as Query-based Span Prediction WEI WU et. al. In this paper, we present CorefQA, an accurate and extensible approach for the coreference resolution task. CS.acl 2020-06-20
148 PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable SIQI BAO et. al. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. CS.acl 2020-06-20
149 Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset XIANG YUE et. al. In this paper, we provide an in-depth analysis of this dataset and the clinical reading comprehension (CliniRC) task. CS.acl 2020-06-20
150 Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering VIKAS YADAV et. al. We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment approach to soft-align questions and answers with justification sentences using only GloVe embeddings, (b) an iterative process that reformulates queries focusing on terms that are not covered by existing justifications, which (c) stops when the terms in the given question and candidate answers are covered by the retrieved justifications. CS.acl 2020-06-20
151 MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization CANWEN XU et. al. We propose MATINF, the first jointly labeled large-scale dataset for classification, question answering and summarization. CS.acl 2020-06-20
152 BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension MIKE LEWIS et. al. We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. CS.acl 2020-06-20
153 Interactive Machine Comprehension with Information Seeking Agents XINGDI YUAN et. al. In this paper, we propose a simple method that reframes existing MRC datasets as interactive, partially observable environments. CS.acl 2020-06-20
154 RikiNet: Reading Wikipedia Pages for Natural Question Answering DAYIHENG LIU et. al. In this paper, we introduce a new model, called RikiNet, which reads Wikipedia pages for natural question answering. CS.acl 2020-06-20
155 Incorporating External Knowledge through Pre-training for Natural Language to Code Generation FRANK F. XU et. al. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. CS.acl 2020-06-20
156 Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering MING YAN et. al. To address this challenge, we propose a multi-source meta transfer (MMT) for low-resource MCQA. CS.acl 2020-06-20
157 GLUECoS: An Evaluation Benchmark for Code-Switched NLP SIMRAN KHANUJA et. al. We present an evaluation benchmark, GLUECoS, for code-switched languages, that spans several NLP tasks in English-Hindi and English-Spanish. CS.acl 2020-06-20
158 ClarQ: A large-scale and diverse dataset for Clarification Question Generation VAIBHAV KUMAR et. al. In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange. CS.acl 2020-06-20
159 A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19 DAVID ONIANI et. al. In this paper, we propose to apply a language model for automatically answering questions related to COVID-19 and qualitatively evaluate the generated responses. CS.arxiv 2020-06-19
160 Results of the seventh edition of the BioASQ Challenge ANASTASIOS NENTIDIS et. al. The results of the seventh edition of the BioASQ challenge are presented in this paper. CS.arxiv 2020-06-16
161 CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization ANDRE ESTEVA et. al. Here we present CO-Search, a retriever-ranker semantic search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers during a time of crisis. CS.arxiv 2020-06-16
162 ORD: Object Relationship Discovery for Visual Dialogue Generation ZIWEI WANG et. al. In this paper, we propose an object relationship discovery (ORD) framework to preserve the object interactions for visual dialogue generation. CS.arxiv 2020-06-15
163 Mining Implicit Relevance Feedback from User Behavior for Web Question Answering LINJUN SHOU et. al. In this paper, we make the first study to explore the correlation between user behavior and passage relevance, and propose a novel approach for mining training data for Web QA. CS.arxiv 2020-06-13
164 Sparse and Continuous Attention Mechanisms ANDRÉ F. T. MARTINS et. al. Sparse and Continuous Attention Mechanisms CS.arxiv 2020-06-12
165 Video Understanding as Machine Translation BRUNO KORBAR et. al. In this work we remove the need for negative sampling by taking a generative modeling approach that poses the objective as a translation problem between modalities. CS.arxiv 2020-06-12
166 Large-Scale Adversarial Training for Vision-and-Language Representation Learning ZHE GAN et. al. We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. CS.arxiv 2020-06-11
167 Exploring Weaknesses of VQA Models through Attribution Driven Insights SHAUNAK HALBE Recent research effectively applies these VQA models for answering visual questions for the blind. CS.arxiv 2020-06-11
168 Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images RUINING DENG et. al. In this paper, we propose a novel Multi-Object Association for Pathology in 3D (Map3D) method for automatically identifying and associating large-scale cross-sections of 3D objects from routine serial sectioning and WSI. CS.arxiv 2020-06-10
169 Estimating semantic structure for the VQA answer space CORENTIN KERVADEC et. al. We address this issue by proposing (1) two measures of proximity between VQA classes, and (2) a corresponding loss which takes into account the estimated proximity. CS.arxiv 2020-06-10
170 ClarQ: A large-scale and diverse dataset for Clarification Question Generation VAIBHAV KUMAR et. al. In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange. We release this dataset in order to foster research into the field of clarification question generation with the larger goal of enhancing dialog and question answering systems. CS.arxiv 2020-06-10
171 Consolidating Commonsense Knowledge FILIP ILIEVSKI et. al. In this paper, we list representative sources and their properties. CS.arxiv 2020-06-10
172 Roses Are Red, Violets Are Blue… but Should Vqa Expect Them To? CORENTIN KERVADEC et. al. In this work, we design a new benchmark based on a fine-grained reorganization of the GQA dataset [1], which allows to precisely answer these questions. CS.arxiv 2020-06-09
173 Knowledge-Aided Open-Domain Question Answering MANTONG ZHOU et. al. In this work, we propose a knowledge-aided open-domain QA (KAQA) method which targets at improving relevant document retrieval and candidate answer reranking by considering the relationship between a question and the documents (termed as question-document graph), and the relationship between candidate documents (termed as document-document graph). CS.arxiv 2020-06-09
174 Foreshadowing the Benefits of Incidental Supervision HANGFENG HE et. al. To quantify the benefits of various incidental supervision signals, we propose a unified PAC-Bayesian Informativeness measure (PABI), characterizing the reduction in uncertainty that incidental supervision signals provide. CS.arxiv 2020-06-09
175 TA-Student VQA: Multi-Agents Training by Self-Questioning PEIXI XIONG et. al. We introduce our self-questioning model with multi-agent training: TA-student VQA. CS.cvpr 2020-06-08
176 Hypergraph Attention Networks for Multimodal Learning EUN-SOL KIM et. al. To resolve this problem, we propose Hypergraph Attention Networks (HANs), which define a common semantic space among the modalities with symbolic graphs and extract a joint representation of the modalities based on a co-attention map constructed in the semantic space. CS.cvpr 2020-06-08
177 Assessing Image Quality Issues for Real-World Problems TAI-YIN CHIU et. al. We introduce a new large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering. CS.cvpr 2020-06-08
178 On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering XINYU WANG et. al. We present a dataset that takes a step towards addressing this problem in that it contains questions expressed in two languages, and an evaluation process that co-opts a well understood image-based metric to reflect the method’s ability to reason. CS.cvpr 2020-06-08
179 Normalized and Geometry-Aware Self-Attention Network for Image Captioning LONGTENG GUO et. al. In this paper, we improve SA from two aspects to promote the performance of image captioning. CS.cvpr 2020-06-08
180 In Defense of Grid Features for Visual Question Answering HUAIZU JIANG et. al. In this paper, we revisit grid features for VQA, and find they can work surprisingly well — running more than an order of magnitude faster with the same accuracy (e.g. if pre-trained in a similar fashion). CS.cvpr 2020-06-08
181 ActBERT: Learning Global-Local Video-Text Representations LINCHAO ZHU et. al. In this paper, we introduce ActBERT for self-supervised learning of joint video-text representations from unlabeled data. CS.cvpr 2020-06-08
182 Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing VEDIKA AGARWAL et. al. In this paper, we propose a novel way to analyze and measure the robustness of the state of the art models w.r.t semantic visual variations as well as propose ways to make models more robust against spurious correlations. CS.cvpr 2020-06-08
183 Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text DIFEI GAO et. al. Following this idea, we propose a novel VQA approach, Multi-Modal Graph Neural Network (MM-GNN). CS.cvpr 2020-06-08
184 Hierarchical Conditional Relation Networks for Video Question Answering THAO MINH LE et. al. We introduce a general-purpose reusable neural unit called Conditional Relation Network (CRN) that serves as a building block to construct more sophisticated structures for representation and reasoning over video. CS.cvpr 2020-06-08
185 Attention-Based Context Aware Reasoning for Situation Recognition THILINI COORAY et. al. Inspired by the success achieved by query-based visual reasoning (e.g., Visual Question Answering), we propose to address semantic role prediction as a query-based visual reasoning problem. CS.cvpr 2020-06-08
186 12-in-1: Multi-Task Vision and Language Representation Learning JIASEN LU et. al. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task model. CS.cvpr 2020-06-08
187 X-Linear Attention Networks for Image Captioning YINGWEI PAN et. al. In this paper, we introduce a unified attention block — X-Linear attention block, that fully employs bilinear pooling to selectively capitalize on visual information or perform multi-modal reasoning. CS.cvpr 2020-06-08
188 Counterfactual Samples Synthesizing for Robust Visual Question Answering LONG CHEN et. al. To this end, we propose a model-agnostic Counterfactual Samples Synthesizing (CSS) training scheme. CS.cvpr 2020-06-08
189 Counterfactual Vision and Language Learning EHSAN ABBASNEJAD et. al. We propose a method that addresses this problem by introducing counterfactuals in the training. CS.cvpr 2020-06-08
190 Language Models as Fact Checkers? NAYEON LEE et. al. In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components. CS.arxiv 2020-06-07
191 SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network RUIXING WANG et. al. In this work, we propose an effective and efficient method named as SERank which is a Sequencewise Ranking model by using Squeeze-and-Excitation network to take advantage of cross-document information. CS.arxiv 2020-06-07
192 Counterfactual VQA: A Cause-Effect Look at Language Bias YULEI NIU et. al. In this paper, we propose a novel cause-effect look at the language bias, where the bias is formulated as the direct effect of question on answer from the view of causal inference. CS.arxiv 2020-06-07
193 Pre-training Polish Transformer-based Language Models at Scale SŁAWOMIR DADAS et. al. In this study, we present two language models for Polish based on the popular BERT architecture. CS.arxiv 2020-06-07
194 A Cross-Task Analysis of Text Span Representations SHUBHAM TOSHNIWAL et. al. In this paper, we conduct a comprehensive empirical evaluation of six span representation methods using eight pretrained language representation models across six tasks, including two tasks that we introduce. CS.arxiv 2020-06-06
195 BERT Based Multilingual Machine Comprehension in English and Hindi SOMIL GUPTA et. al. We, therefore, present in this article, our experiments with m-BERT for MMC in zero-shot, mono-lingual (e.g. Hindi Question-Hindi Snippet) and cross-lingual (e.g. English QuestionHindi Snippet) fine-tune setups. CS.arxiv 2020-06-02
196 Question Answering on Scholarly Knowledge Graphs MOHAMAD YASER JARADEH et. al. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Furthermore, we present a preliminary dataset of related tables and a corresponding set of natural language questions. CS.arxiv 2020-06-02
197 Probing Emergent Semantics in Predictive Agents via Question Answering ABHISHEK DAS et. al. We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modeling -action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019). CS.arxiv 2020-06-01
198 Context-based Transformer Models for Answer Sentence Selection IVANO LAURIOLA et. al. In this paper, we analyze the role of the contextual information in the sentence selection task, proposing a Transformer based architecture that leverages two types of contexts, local and global. CS.arxiv 2020-06-01
199 Multimodal grid features and cell pointers for Scene Text Visual Question Answering LLUÍS GÓMEZ et. al. This paper presents a new model for the task of scene text visual question answering, in which questions about a given image can only be answered by reading and understanding scene text that is present in it. CS.arxiv 2020-06-01
200 Structured Multimodal Attentions for TextVQA CHENYU GAO et. al. In this paper, we propose a structured multimodal attention (SMA) neural network to solve the above issues. CS.arxiv 2020-06-01
201 An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features SHI-YAN WENG et. al. In view of this, we in this paper contextualize and enhance the state-of-the-art BERT-based model for speech summarization, while its contributions are at least three-fold. CS.arxiv 2020-06-01
202 Conversational Machine Comprehension: a Literature Review SOMIL GUPTA et. al. It focuses on synthesizing a generic framework for CMC models, rather than describing the models individually. CS.arxiv 2020-05-31
203 Efficient Deployment of Conversational Natural Language Interfaces over Databases ANTHONY COLAS et. al. In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models. CS.arxiv 2020-05-31
204 Improving Quality of a Post’s Set of Answers in Stack Overflow MOHAMMADREZAR TAVAKOLI et. al. In this paper, we propose an approach to automate the identification process of such posts and boost their set of answers, utilizing the help of related experts. CS.arxiv 2020-05-30
205 Using Large Pretrained Language Models for Answering User Queries from Product Specifications KALYANI ROY et. al. We propose an approach to automatically create a training dataset for this problem. CS.arxiv 2020-05-29
206 Language Models are Few-Shot Learners TOM B. BROWN et. al. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. CS.arxiv 2020-05-28
207 Object-QA: Towards High Reliable Object Quality Assessment JING LU et. al. In this paper, we define the problem of object quality assessment for the first time and propose an effective approach named Object-QA to assess high-reliable quality scores for object images. CS.arxiv 2020-05-26
208 English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too JASON PHANG et. al. Here, we investigate whether English intermediate-task training is still helpful on non-English target tasks in a zero-shot cross-lingual setting. CS.arxiv 2020-05-26
209 Generating Semantically Valid Adversarial Questions for TableQA YI ZHU et. al. In this paper, we propose SAGE (Semantically valid Adversarial GEnerator), a Wasserstein sequence-to-sequence model for TableQA white-box attack. CS.arxiv 2020-05-26
210 Refining Implicit Argument Annotation For UCCA RUIXIANG CUI et. al. In this paper, we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation’s foundational layer (Abend and Rappoport, 2013). CS.arxiv 2020-05-26
211 Knowledge Graph Simple Question Answering for Unseen Domains GEORGIOS SIDIROPOULOS et. al. We propose a data-centric domain adaptation framework that consists of a KGSQA system that is applicable to new domains, and a sequence to sequence question generation method that automatically generates question-answer pairs for the new domain. CS.arxiv 2020-05-25
212 An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering CHIA-CHIH KUO et. al. Consequently, an audio-enriched BERT-based SMCQA framework is proposed. CS.arxiv 2020-05-25
213 History-Aware Question Answering in a Blocks World Dialogue System BENJAMIN KANE et. al. In this paper, we approach the problem of spatial memory in a multi-modal spoken dialogue system capable of answering questions about interaction history in a physical blocks world setting. CS.arxiv 2020-05-25
214 Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks PATRICK LEWIS et. al. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. CS.arxiv 2020-05-22
215 Comparative Study of Machine Learning Models and BERT on SQuAD DEVSHREE PATEL et. al. This study aims to provide a comparative analysis of performance of certain models popular in machine learning and the BERT model on the Stanford Question Answering Dataset (SQuAD). CS.arxiv 2020-05-22
216 A Complex KBQA System using Multiple Reasoning Paths KECHEN QIN et. al. In this paper, we introduce an end-to-end KBQA system which can leverage multiple reasoning paths’ information and only requires labeled answer as supervision. CS.arxiv 2020-05-21
217 RuBQ: A Russian Dataset for Question Answering over Wikidata VLADISLAV KORABLINOV et. al. The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset. CS.arxiv 2020-05-21
218 Functorial Language Games for Question Answering GIOVANNI DE FELICE et. al. We present some categorical investigations into Wittgenstein’s language-games, with applications to game-theoretic pragmatics and question-answering in natural language processing. CS.arxiv 2020-05-19
219 On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law DAMIEN TENEY et. al. We provide short- and long-term solutions to avoid these pitfalls and realize the benefits of OOD evaluation. CS.arxiv 2020-05-19
220 Matching Questions and Answers in Dialogues from Online Forums QI JIA et. al. This paper presents a QA matching model considering both distance information and dialogue history by two simultaneous attention mechanisms called mutual attention. Because existing dialogue datasets such as the Ubuntu dataset are not suitable for the QA matching task, we further create a dataset with 1,000 labeled dialogues and demonstrate that our proposed model outperforms the state-of-the-art and other strong baselines, particularly for matching long-distance QA pairs. CS.arxiv 2020-05-19
221 Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite Tasks SWAROOP MISHRA et. al. In pursuit of this goal, we introduce NUMBERGAME, a multifaceted benchmark to evaluate model performance across numerical reasoning tasks of eight diverse formats. CS.arxiv 2020-05-18
222 Question-Driven Summarization of Answers to Consumer Health Questions MAX SAVERY et. al. Using answers provided by the National Library of Medicine’s consumer health question answering system, we present the MEDIQA Answer Summarization dataset, the first summarization collection containing question-driven summaries of answers to consumer health questions. CS.arxiv 2020-05-18
223 Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer BHASKAR SEN et. al. In this brief paper, we tackle the quality Q&A modeling problems from the community support websites using a recently developed deep learning model using bidirectional transformers. CS.arxiv 2020-05-17
224 Context-Based Quotation Recommendation ANSEL MACLAUGHLIN et. al. We therefore propose a novel context-aware quote recommendation system which utilizes the content an author has already written to generate a ranked list of quotable paragraphs and spans of tokens from a given source document. CS.arxiv 2020-05-17
225 CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task SIRWE SAEEDI et. al. In this paper, we investigate a commonsense inference task that unifies natural language understanding and commonsense reasoning. CS.arxiv 2020-05-17
226 Visual Relationship Detection using Scene Graphs: A Survey ANIKET AGARWAL et. al. In this paper, we present a detailed survey on the various techniques for scene graph generation, their efficacy to represent visual relationships and how it has been used to solve various downstream tasks. CS.arxiv 2020-05-16
227 COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter MARTIN MÜLLER et. al. In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. CS.arxiv 2020-05-15
228 An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition GIZEM ARAS et. al. In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory and Transformer-based networks) proposed for Turkish NER tagging in the same setting. CS.arxiv 2020-05-14
229 Recommending Themes for Ad Creative Design via Visual-Linguistic Representations YICHAO ZHOU et. al. To automatically infer ad themes via such multimodal sources of information in past ad campaigns, we propose a theme (keyphrase) recommender system for ad creative strategists. CS.www 2020-05-13
230 Complex Factoid Question Answering with a Free-Text Knowledge Graph CHEN ZHAO et. al. We introduce delft, a factoid question answering system which combines the nuance and depth of knowledge graph question answering approaches with the broader coverage of free-text. CS.www 2020-05-13
231 Interpretable Complex Question Answering SOUMEN CHAKRABARTI We will review cross-community co-evolution of question answering (QA) with the advent of large-scale knowledge graphs (KGs), continuous representations of text and graphs, and deep sequence analysis. CS.www 2020-05-13
232 Entity-Enriched Neural Models for Clinical Question Answering BHANU PRATAP SINGH RAWAT et. al. We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) questions at test time. CS.arxiv 2020-05-13
233 Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs TRUNG-KIEN TRAN et. al. In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns. CS.www 2020-05-13
234 What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization CALEB BELTH et. al. In this work, we introduce a unified solution to KG characterization by formulating the problem as unsupervised KG summarization with a set of inductive, soft rules, which describe what is normal in a KG, and thus can be used to identify what is abnormal, whether it be strange or missing. CS.www 2020-05-13
235 Text-to-SQL Generation for Question Answering on Electronic Medical Records PING WANG et. al. Based on the widely used publicly available electronic medical database, we create a new large-scale Question-SQL pair dataset, named MIMICSQL, in order to perform the Question-to-SQL generation task in healthcare domain. CS.www 2020-05-13
236 Condition Aware and Revise Transformer for Question Answering XINYAN ZHAO et. al. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CS.www 2020-05-13
237 Distant Supervision for Multi-Stage Fine-Tuning in Retrieval-Based Question Answering YUQING XIE et. al. In the context of this architecture, we present a data augmentation technique using distant supervision to automatically annotate paragraphs as either positive or negative examples to supplement existing training data, which are then used together to fine-tune BERT. CS.www 2020-05-13
238 Voice-based Reformulation of Community Answers SIMONE FILICE et. al. In this paper, we define the Answer Reformulation task and propose a novel solution to automatically reformulate a community provided answer making it suitable for a voice interaction. CS.www 2020-05-13
239 Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus BANG LIU et. al. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. CS.www 2020-05-13
240 Review-guided Helpful Answer Identification in E-commerce WENXUAN ZHANG et. al. In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds’ opinions reflected in the reviews, which is another important factor to identify helpful answers. CS.www 2020-05-13
241 Do not let the history haunt you — Mitigating Compounding Errors in Conversational Question Answering ANGROSH MANDYA et. al. In this paper, we find that compounding errors occur when using previously predicted answers at test time, significantly lowering the performance of CoQA systems. CS.arxiv 2020-05-12
242 A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering NILS HOLZENBERGER et. al. To investigate the performance of natural language understanding approaches on statutory reasoning, we introduce a dataset, together with a legal-domain text corpus. CS.arxiv 2020-05-11
243 Maximizing Information Gain in Partially Observable Environments via Prediction Reward YASH SATSANGI et. al. Based on this insight we present deep anticipatory networks (DANs), which enables an agent to take actions to reduce its uncertainty without performing explicit belief inference. CS.arxiv 2020-05-11
244 Quantum Annealing-Based Software Components: An Experimental Case Study with SAT Solving TOM KRÜGER et. al. In this paper, we perform a case study on how to augment existing software with quantum computational primitives for the Boolean satisfiability problem (SAT) implemented using a quantum annealer (QA). CS.arxiv 2020-05-11
245 How Context Affects Language Models’ Factual Predictions FABIO PETRONI et. al. In this paper, we go a step further and integrate information from a retrieval system with a pre-trained language model in a purely unsupervised way. CS.arxiv 2020-05-10
246 Where is Linked Data in Question Answering over Linked Data? TOMMASO SORU et. al. To this end, we propose the creation of new evaluation settings to leverage the advantages of the Semantic Web to achieve AI-complete question answering. CS.arxiv 2020-05-07
247 COBRA: Contrastive Bi-Modal Representation Algorithm VISHAAL UDANDARAO et. al. In this paper, we present a novel framework COBRA that aims to train two modalities (image and text) in a joint fashion inspired by the Contrastive Predictive Coding (CPC) and Noise Contrastive Estimation (NCE) paradigms which preserve both inter and intra-class relationships. CS.arxiv 2020-05-07
248 DramaQA: Character-Centered Video Story Understanding with Hierarchical QA SEONGHO CHOI et. al. In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. CS.arxiv 2020-05-07
249 CounQER: A System for Discovering and Linking Count Information in Knowledge Bases SHRESTHA GHOSH et. al. CounQER: A System for Discovering and Linking Count Information in Knowledge Bases CS.arxiv 2020-05-07
250 MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models MANDY GUO et. al. This paper presents MultiReQA, anew multi-domain ReQA evaluation suite com-posed of eight retrieval QA tasks drawn from publicly available QA datasets. CS.arxiv 2020-05-05
251 DoQA — Accessing Domain-Specific FAQs via Conversational QA JON ANDER CAMPOS et. al. The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. CS.arxiv 2020-05-04
252 What-if I ask you to explain: Explaining the effects of perturbations in procedural text DHEERAJ RAJAGOPAL et. al. We present QUARTET, a system that constructs such explanations from paragraphs, by modeling the explanation task as a multitask learning problem. CS.arxiv 2020-05-04
253 Visual Question Answering with Prior Class Semantics VIOLETTA SHEVCHENKO et. al. We present a novel mechanism to embed prior knowledge in a model for visual question answering. CS.arxiv 2020-05-03
254 SEEK: Segmented Embedding of Knowledge Graphs WENTAO XU et. al. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. CS.arxiv 2020-05-02
255 ForecastQA: Machine Comprehension of Temporal Text for Answering Forecasting Questions WOOJEONG JIN et. al. In this paper, we introduce ForecastQA, a new open-domain question answering dataset consisting of 10k questions which requires temporal reasoning. CS.arxiv 2020-05-02
256 Measuring and Reducing Non-Multifact Reasoning in Multi-hop Question Answering HARSH TRIVEDI et. al. To this end, we introduce an automated sufficiency-based dataset transformation that considers all possible partitions of supporting facts, capturing disconnected reasoning. CS.arxiv 2020-05-02
257 AVA: an Automatic eValuation Approach to Question Answering Systems THUY VU et. al. We introduce AVA, an automatic evaluation approach for Question Answering, which given a set of questions associated with Gold Standard answers, can estimate system Accuracy. To design, train and test AVA, we built multiple large training, development, and test sets on both public and industrial benchmarks. CS.arxiv 2020-05-02
258 UnifiedQA: Crossing Format Boundaries With a Single QA System DANIEL KHASHABI et. al. As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats. CS.arxiv 2020-05-02
259 ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning MICHAEL BORATKO et. al. This paper introduces a new question answering dataset for training and evaluating common-sense reasoning capabilities of artificial intelligence systems in such prototypical situations. CS.arxiv 2020-05-02
260 BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA NORA KASSNER et. al. Our contributions are as follows. CS.arxiv 2020-05-02
261 Exploring and Predicting Transferability across NLP Tasks TU VU et. al. In this paper, we conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of problems (text classification, question answering, and sequence labeling). CS.arxiv 2020-05-02
262 From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ANNE LAUSCHER et. al. In this work, we analyze their limitations and show that cross-lingual transfer via massively multilingual transformers, much like transfer via cross-lingual word embeddings, is substantially less effective in resource-lean scenarios and for distant languages. CS.arxiv 2020-05-01
263 Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering YANLIN FENG et. al. In this paper, we propose a novel knowledge-aware approach that equips PTLMs with a multi-hop relational reasoning module, named multi-hop graph relation networks (MHGRN). CS.arxiv 2020-05-01
264 Self-supervised Knowledge Triplet Learning for Zero-shot Question Answering PRATYAY BANERJEE et. al. In this work, we propose Knowledge Triplet Learning, a self-supervised task over knowledge graphs. CS.arxiv 2020-05-01
265 Diverse Visuo-Lingustic Question Answering (DVLQA) Challenge SHAILAJA SAMPAT et. al. Diverse Visuo-Lingustic Question Answering (DVLQA) Challenge CS.arxiv 2020-05-01
266 Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering PEIFENG WANG et. al. In this paper, we propose to learn a multi-hop knowledge path generator to generate structured evidence dynamically according to the questions. CS.arxiv 2020-05-01
267 Exploiting Social Networks. Technological Trends (Habilitation Thesis) ADRIAN IFTENE The habilitation thesis presents two main directions: 1. CS.arxiv 2020-04-30
268 How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking NICOLA DE CAO et. al. To deal with these two challenges, we introduce Differentiable Masking. CS.arxiv 2020-04-30
269 Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering WENHAN XIONG et. al. In this work, we propose a simple and resource-efficient method to pretrain the paragraph encoder. CS.arxiv 2020-04-30
270 Context based Text-generation using LSTM networks SIVASURYA SANTHANAM Several methods of extracting the context vectors are proposed in this work. CS.arxiv 2020-04-30
271 MAD-X: An Adapter-based Framework for Multi-task Cross-lingual Transfer JONAS PFEIFFER et. al. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. CS.arxiv 2020-04-30
272 KPQA: A Metric for Generative Question Answering Using Word Weights HWANHEE LEE et. al. To alleviate this problem, we propose a new metric for evaluating the correctness of genQA. CS.arxiv 2020-04-30
273 Look at the First Sentence: Position Bias in Question Answering MIYOUNG KO et. al. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions learn spurious positional cues and fail to give answers in different positions. CS.arxiv 2020-04-30
274 Question Rewriting for Conversational Question Answering SVITLANA VAKULENKO et. al. We address the conversational QA task by decomposing it into question rewriting and question answering subtasks, and conduct a systematic evaluation of this approach on two publicly available datasets. CS.arxiv 2020-04-30
275 Robust Question Answering Through Sub-part Alignment JIFAN CHEN et. al. To make a more robust and understandable QA system, we model question answering as an alignment problem. CS.arxiv 2020-04-30
276 HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training LINJIE LI et. al. We present HERO, a Hierarchical EncodeR for Omni-representation learning, for large-scale video+language pre-training. CS.arxiv 2020-04-30
277 A Focused Study to Compare Arabic Pre-training Models on Newswire IE Tasks WUWEI LAN et. al. In this work, we pre-train a Gigaword-based bilingual language model (GigaBERT) to study these two distant languages as well as zero-short transfer learning on the information extraction tasks. CS.arxiv 2020-04-29
278 A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT MASAAKI NAGATA et. al. We present a novel supervised word alignment method based on cross-language span prediction. CS.arxiv 2020-04-29
279 Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning TAO SHEN et. al. In this work, we aim at equipping pre-trained language models with structured knowledge. CS.arxiv 2020-04-29
280 Exploring Contextualized Neural Language Models for Temporal Dependency Parsing HAYLEY ROSS et. al. In this paper, we developed several variants of BERT-based temporal dependency parser, and show that BERT significantly improves temporal dependency parsing (Zhang and Xue,2018a). CS.arxiv 2020-04-29
281 SubjQA: A Dataset for Subjectivity and Review Comprehension JOHANNES BJERVA et. al. We therefore investigate the relationship between subjectivity and QA, while developing a new dataset. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 distinct domains. CS.arxiv 2020-04-29
282 Pragmatic Issue-Sensitive Image Captioning ALLEN NIE et. al. To address this, we propose Issue-Sensitive Image Captioning (ISIC). CS.arxiv 2020-04-29
283 The Effect of Natural Distribution Shift on Question Answering Models JOHN MILLER et. al. We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data. CS.arxiv 2020-04-29
284 Event Extraction by Answering (Almost) Natural Questions XINYA DU et. al. To avoid this issue, we introduce a new paradigm for event extraction by formulating it as a question answering (QA) task, which extracts the event arguments in an end-to-end manner. CS.arxiv 2020-04-28
285 Empower Entity Set Expansion via Language Model Probing YUNYI ZHANG et. al. In this study, we propose a novel iterative set expansion framework that leverages automatically generated class names to address the semantic drift issue. CS.arxiv 2020-04-28
286 A Novel Attention-based Aggregation Function to Combine Vision and Language MATTEO STEFANINI et. al. In this paper, we propose a novel fully-attentive reduction method for vision and language. CS.arxiv 2020-04-27
287 Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System XINYUE ZHENG et. al. Inspired by the dual process theory in cognitive science, we propose a MetaQA framework, where system 1 is an intuitive meta-classifier and system 2 is a reasoning module. CS.arxiv 2020-04-26
288 A Neural Document Language Modeling Framework for Spoken Document Retrieval L. YEN et. al. Targeting on enhancing the SDR performance, the paper concentrates on proposing a neural retrieval framework, which assembles the merits of using language modeling (LM) mechanism in SDR and leveraging the abstractive information learned by the language representation models. CS.icassp 2020-04-26
289 GLUECoS : An Evaluation Benchmark for Code-Switched NLP SIMRAN KHANUJA et. al. We present an evaluation benchmark, GLUECoS, for code-switched languages, that spans several NLP tasks in English-Hindi and English-Spanish. CS.arxiv 2020-04-26
290 Multi-Layer Content Interaction Through Quaternion Product for Visual Question Answering L. SHI ET AL. To solve the issue for the intermediate layers, we propose an efficient Quaternion Block Network (QBN) to learn interaction not only for the last layer but also for all intermediate layers simultaneously. CS.icassp 2020-04-26
291 Knowledge Enhanced Latent Relevance Mining for Question Answering D. WANG et. al. In this paper, we propose to integrate commonsense from the external knowledge graphs (KGs) into the answer selector through a knowledge-aware context-based attention mechanism. CS.icassp 2020-04-26
292 Improving Spoken Question Answering Using Contextualized Word Representation D. SU AND P. FUNG In this paper, we propose using contextualized word representations to mitigate the effects of ASR errors and pretraining on existing textual QA datasets to mitigate the data scarcity issue. CS.icassp 2020-04-26
293 A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts WANJUN ZHONG et. al. In this work, we propose a graph-based approach, where a heterogeneous graph is automatically built with factual knowledge of the context, temporal knowledge of the past states, and logical knowledge that combines human-curated knowledge bases and rule bases. CS.arxiv 2020-04-25
294 MCQA: Multimodal Co-attention Based Network for Question Answering ABHISHEK KUMAR et. al. We present MCQA, a learning-based algorithm for multimodal question answering. CS.arxiv 2020-04-25
295 Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs TARCÍSIO SOUZA COSTA et. al. In this paper we present the Event-QA dataset for answering event-centric questions over knowledge graphs. CS.arxiv 2020-04-24
296 Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering ALEXANDER R. FABBRI et. al. We propose an unsupervised approach to training QA models with generated pseudo-training data. CS.arxiv 2020-04-24
297 Lite Transformer with Long-Short Range Attention ZHANGHAO WU et. al. In this paper, we present an efficient mobile NLP architecture, Lite Transformer to facilitate deploying mobile NLP applications on edge devices. CS.arxiv 2020-04-24
298 Revisiting Modulated Convolutions for Visual Counting and Beyond DUY-KIEN NGUYEN et. al. In this paper, we propose a simple and effective alternative for visual counting by revisiting modulated convolutions that fuse query and image locally. CS.arxiv 2020-04-24
299 Contextualized Representations Using Textual Encyclopedic Knowledge MANDAR JOSHI et. al. We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents. CS.arxiv 2020-04-24
300 Rapidly Bootstrapping a Question Answering Dataset for COVID-19 RAPHAEL TANG et. al. We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle’s COVID-19 Open Research Dataset Challenge. CS.arxiv 2020-04-23
301 Visual Question Answering Using Semantic Information from Image Descriptions TASMIA TASRIN et. al. Considering these, we propose a deep neural network model that uses an attention mechanism which utilizes image features, the natural language question asked and semantic knowledge extracted from the image to produce open-ended answers for the given questions. CS.arxiv 2020-04-23
302 AmbigQA: Answering Ambiguous Open-domain Questions SEWON MIN et. al. In this paper, we introduce AmbigQA, a new open-domain question answering task which involves predicting a set of question-answer pairs, where every plausible answer is paired with a disambiguated rewrite of the original question. CS.arxiv 2020-04-22
303 TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce SHUANGYONG SONG et. al. In this paper, we aim to improve an IR based e-commerce QA system-AliMe with proposed text matching models, including a basic Triple Convolutional Neural Network (TCNN) model and two Attention-based TCNN (ATCNN) models. CS.arxiv 2020-04-22
304 Optimizing Knowledge Graphs through Voting-based User Feedback R. YANG et. al. To address these issues, in this paper, we propose an interactive framework that refines and optimizes knowledge graphs through user votes. CS.icde 2020-04-20
305 Software Test Automation Maturity — A Survey of the State of the Practice YUQING WANG et. al. In this paper, we present a test automation maturity survey serving as a self-assessment for practitioners. CS.arxiv 2020-04-20
306 Word Embedding-based Text Processing for Comprehensive Summarization and Distinct Information Extraction XIANGPENG WAN et. al. In this paper, we propose two automated text processing frameworks specifically designed to analyze online reviews. CS.arxiv 2020-04-20
307 Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network GUANMING XIONG In this paper, we present a two stage model for multi-hop question answering. CS.arxiv 2020-04-20
308 Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision DAMIEN TENEY et. al. We propose an auxiliary training objective that improves the generalization capabilities of neural networks by leveraging an overlooked supervisory signal found in existing datasets. CS.arxiv 2020-04-19
309 ktrain: A Low-Code Library for Augmented Machine Learning ARUN S. MAIYA We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply. CS.arxiv 2020-04-19
310 Bridging Anaphora Resolution as Question Answering YUFANG HOU In this paper, we cast bridging anaphora resolution as question answering based on context. CS.arxiv 2020-04-16
311 A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation JAN DERIU et. al. In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. CS.arxiv 2020-04-16
312 Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework JIEHANG ZENG et. al. We present a learning method using generative adversarial architecture designed to embed the entities and relations of the knowledge graphs into a continuous vector space. CS.arxiv 2020-04-15
313 HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data WENHU CHEN et. al. To fill in the gap, we present \dataset, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. CS.arxiv 2020-04-15
314 A Simple Yet Strong Pipeline for HotpotQA DIRK GROENEVELD et. al. Our pipeline has three steps: 1) use BERT to identify potentially relevant sentences independently of each other; 2) feed the set of selected sentences as context into a standard BERT span prediction model to choose an answer; and 3) use the sentence selection model, now with the chosen answer, to produce supporting sentences. CS.arxiv 2020-04-14
315 Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension ADYASHA MAHARANA et. al. We address this issue via RL and more efficient Bayesian policy search methods for automatically learning the best augmentation policy combinations of the transformation probability for each adversary in a large search space. CS.arxiv 2020-04-13
316 YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos SHIZHE CHEN et. al. In this paper, we present the challenge guidelines, the dataset used, and performances of baseline models on the two proposed tasks. CS.arxiv 2020-04-12
317 Explaining Question Answering Models through Text Generation VERONICA LATCINNIK et. al. In this work, we propose a model for multi-choice question answering, where a LM-based generator generates a textual hypothesis that is later used by a classifier to answer the question. CS.arxiv 2020-04-12
318 Which visual questions are difficult to answer? Analysis with Entropy of Answer Distributions KENTO TERAO et. al. We propose a novel approach to identify the difficulty of visual questions for Visual Question Answering (VQA) without direct supervision or annotations to the difficulty. CS.arxiv 2020-04-12
319 Unsupervised Commonsense Question Answering with Self-Talk VERED SHWARTZ et. al. We propose an unsupervised framework based on \emph{self-talk} as a novel alternative to multiple-choice commonsense tasks. CS.arxiv 2020-04-11
320 Exploring The Spatial Reasoning Ability of Neural Models in Human IQ Tests HYUNJAE KIM et. al. In this work, we focus on spatial reasoning and explore the spatial understanding of neural models. Using well-defined rules, we constructed datasets that consist of various complexity levels. CS.arxiv 2020-04-11
321 Rephrasing visual questions by specifying the entropy of the answer distribution KENTO TERAO et. al. We propose two learning strategies to train the proposed model with the VQA v2 dataset, which has no ambiguity information. CS.arxiv 2020-04-10
322 Dense Passage Retrieval for Open-Domain Question Answering VLADIMIR KARPUKHIN et. al. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. CS.arxiv 2020-04-10
323 Towards Automatic Generation of Questions from Long Answers SHLOK KUMAR MISHRA et. al. Therefore, we propose a novel evaluation benchmark to assess the performance of existing AQG systems for long-text answers. CS.arxiv 2020-04-10
324 Natural Perturbation for Robust Question Answering DANIEL KHASHABI et. al. As an alternative to the standard approach of addressing this issue by constructing training sets of completely new examples, we propose doing so via minimal perturbation of examples. CS.arxiv 2020-04-09
325 A Multilingual Study of Multi-Sentence Compression using Word Vertex-Labeled Graphs and Integer Linear Programming ELVYS LINHARES PONTES et. al. This paper describes an Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, with the goal of generating more informative sentences while maintaining their grammaticality. CS.arxiv 2020-04-09
326 What do Models Learn from Question Answering Datasets? PRIYANKA SEN et. al. In this paper, we investigate what models are really learning from QA datasets by evaluating BERT-based models across five popular QA datasets. CS.arxiv 2020-04-07
327 Faithful Embeddings for Knowledge Base Queries HAITIAN SUN et. al. \emph{Query embedding} (QE) techniques have been recently proposed where KB entities and KB queries are represented jointly in an embedding space, supporting relaxation and generalization in KB inference. CS.arxiv 2020-04-07
328 Variational Question-Answer Pair Generation for Machine Reading Comprehension KAZUTOSHI SHINODA et. al. We present a deep generative model of question-answer (QA) pairs for machine reading comprehension. CS.arxiv 2020-04-07
329 Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing GOONMEET BAJAJ et. al. As an initial step towards better quantifying our understanding of the performance of VQA models, we use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs. CS.arxiv 2020-04-07
330 Automated Utterance Generation SOHAM PARIKH et. al. In this paper, we propose an utterance generation system which 1) uses extractive summarization to extract important sentences from the description, 2) uses multiple paraphrasing techniques to generate a diverse set of paraphrases of the title and summary sentences, and 3) selects good candidate paraphrases with the help of a novel candidate selection algorithm. CS.arxiv 2020-04-07
331 Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA NINA POERNER et. al. We cover over 60% of the BioBERT-BERT F1 delta, at 5% of BioBERT’s CO_2 footprint and 2% of its cloud compute cost. CS.arxiv 2020-04-07
332 Enhancing Review Comprehension with Domain-Specific Commonsense AARON TRAYLOR et. al. In this paper, we introduce xSense, an effective system for review comprehension using domain-specific commonsense knowledge bases (xSense KBs). CS.arxiv 2020-04-06
333 Query Focused Multi-Document Summarization with Distant Supervision YUMO XU et. al. In this work, we leverage distant supervision from question answering where various resources are available to more explicitly capture the relationship between queries and documents. CS.arxiv 2020-04-06
334 Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering PRATYAY BANERJEE et. al. In our work, we learn a semantic knowledge ranking model to re-rank knowledge retrieved through Lucene based information retrieval systems. CS.arxiv 2020-04-06
335 Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games YUFEI FENG et. al. We propose a cooperative game approach to deal with this problem, in which how the evidence passages are selected and how the selected passages are connected are handled by two models that cooperate to select the most confident chains from a large set of candidates (from distant supervision). We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i.e., the question-answer pairs. For evaluation, we created benchmarks based on two multi-hop QA datasets, HotpotQA and MedHop; and hand-labeled reasoning chains for the latter. CS.arxiv 2020-04-05
336 Mining Shape of Expertise: A Novel Approach Based on Convolutional Neural Network MAHDI DEHGHAN et. al. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. CS.arxiv 2020-04-05
337 TAPAS: Weakly Supervised Table Parsing via Pre-training JONATHAN HERZIG et. al. In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms. CS.arxiv 2020-04-05
338 Reinforced Multi-task Approach for Multi-hop Question Generation DEEPAK GUPTA et. al. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. CS.arxiv 2020-04-05
339 Natural language processing for word sense disambiguation and information extraction K. R. CHOWDHARY The thesis presents a new approach for Word Sense Disambiguation using thesaurus. CS.arxiv 2020-04-05
340 Evaluating Multimodal Representations on Visual Semantic Textual Similarity OIER LOPEZ DE LACALLE et. al. The long term goal of our research is to devise multimodal representation techniques that improve current inference capabilities. CS.arxiv 2020-04-04
341 Talk to Papers: Bringing Neural Question Answering to Academic Search TIANCHANG ZHAO et. al. We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. CS.arxiv 2020-04-04
342 Generating Rationales in Visual Question Answering HAMMAD A. AYYUBI et. al. Generating Rationales in Visual Question Answering CS.arxiv 2020-04-04
343 Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions ASIA J. BIEGA et. al. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding. CS.arxiv 2020-04-04
344 Template-based Question Answering using Recursive Neural Networks RAM G ATHREYA et. al. We propose a neural network-based approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. CS.arxiv 2020-04-03
345 R3: A Reading Comprehension Benchmark Requiring Reasoning Processes RAN WANG et. al. In this work, we propose a novel task of reading comprehension, in which a model is required to provide final answers and reasoning processes. We develop an annotation platform to facilitate TRMR’s annotation, and release the R3 dataset, a \textbf{R}eading comprehension benchmark \textbf{R}equiring \textbf{R}easoning processes. CS.arxiv 2020-04-02
346 Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers ZHICHENG HUANG et. al. We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding in a unified end-to-end framework. CS.arxiv 2020-04-02
347 A Case Study on Tool Support for Collaboration in Agile Development FABIO CALEFATO et. al. We report on a longitudinal case study conducted at the Italian site of a large software company to further our understanding of how development and communication tools can be improved to better support agile practices and collaboration. CS.arxiv 2020-04-01
348 Unification-based Reconstruction of Explanations for Science Questions MARCO VALENTINO et. al. The paper presents a framework to reconstruct explanations for multiple choices science questions through explanation-centred corpora. CS.arxiv 2020-03-31
349 NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain AYUSH JAIN et. al. In this paper, the Nuclear Bidirectional Encoder Representational Transformers (NukeBERT) is proposed, which incorporates a novel technique for building BERT vocabulary to make it suitable for tasks with less training data. CS.arxiv 2020-03-30
350 P $\approx$ NP, at least in Visual Question Answering SHAILZA JOLLY et. al. In this paper, we measure the potential confounding factors when polar and non-polar samples are used jointly to train a baseline VQA classifier, and compare it to an upper bound where the over-representation of polar questions is excluded from the training. CS.arxiv 2020-03-26
351 Common-Knowledge Concept Recognition for SEVA JITIN KRISHNAN et. al. We use a pre-trained language model and fine-tune it with the labeled dataset of concepts. With the help of a domain expert and text processing methods, we construct a dataset annotated at the word-level by carefully defining a labelling scheme to train a sequence model to recognize systems engineering concepts. In addition, we also create some essential datasets for information such as abbreviations and definitions from the systems engineering domain. CS.arxiv 2020-03-25
352 Linguistically Driven Graph Capsule Network for Visual Question Reasoning QINGXING CAO et. al. Inspired by the property of a capsule network that can carve a tree structure inside a regular convolutional neural network (CNN), we propose a hierarchical compositional reasoning model called the Linguistically driven Graph Capsule Network, where the compositional process is guided by the linguistic parse tree. CS.arxiv 2020-03-22
353 Visual Question Answering for Cultural Heritage PIETRO BONGINI et. al. The advantages are twofold: on the one hand the cognitive burden of the visitor will decrease, limiting the flow of information to what the user actually wants to hear; and on the other hand it proposes the most natural way of interacting with a guide, favoring engagement. CS.arxiv 2020-03-22
354 Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks ALI BALALI et. al. To address the two above-referred problems, we propose a novel joint event extraction framework that aims to extract multiple event triggers and arguments simultaneously by introducing shortest dependency path (SDP) in the dependency graph. CS.arxiv 2020-03-19
355 Beheshti-NER: Persian Named Entity Recognition Using BERT EHSAN TAHER et. al. In this paper, we use the pre-trained deep bidirectional network, BERT, to make a model for named entity recognition in Persian. CS.arxiv 2020-03-19
356 QnAMaker: Data to Bot in 2 Minutes PARAG AGRAWAL et. al. We demonstrate QnAMaker, a service that creates a conversational layer over semi-structured data such as FAQ pages, product manuals, and support documents. CS.arxiv 2020-03-18
357 RSVQA: Visual Question Answering for Remote Sensing Data SYLVAIN LOBRY et. al. This paper introduces the task of visual question answering for remote sensing data (RSVQA). Using an automatic method introduced in this article, we built two datasets (using low and high resolution data) of image/question/answer triplets. CS.arxiv 2020-03-16
358 Towards Ground Truth Evaluation of Visual Explanations AHMED OSMAN et. al. In the present work, we propose instead to evaluate explanations in a restricted and controlled setup using a synthetic dataset of rendered 3D shapes. CS.arxiv 2020-03-16
359 TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding ZHIHENG HUANG et. al. In this paper, we investigate how these two modeling techniques can be combined to create a more powerful model architecture. CS.arxiv 2020-03-15
360 Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning MANDANA SAEBI et. al. We address these problems by introducing a type-enhanced RL agent that uses the local neighborhood information for efficient path-based reasoning over knowledge graphs. CS.arxiv 2020-03-12
361 Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning ZHIYUAN FANG et. al. We present the first work on generating \textit{commonsense} captions directly from videos, in order to describe latent aspects such as intentions, attributes, and effects. CS.arxiv 2020-03-11
362 Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking SAMUEL BROSCHEIT In this study we investigate the following questions: (a) Can all those steps be learned jointly with a model for contextualized text-representations, i.e. BERT (Devlin et al., 2019)? CS.arxiv 2020-03-11
363 TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages JONATHAN H. CLARK et. al. We present TyDi QA—a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. CS.arxiv 2020-03-10
364 MQA: Answering the Question via Robotic Manipulation YUHONG DENG et. al. In this paper,we propose a novel task of Manipulation Question Answering(MQA),a class of Question Answering (QA) task, where the robot is required to find the answer to the question by actively interacting with the environment via manipulation. CS.arxiv 2020-03-10
365 Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA XIANPEI HAN et. al. In this paper, we give an overview of the knowledge graph evaluation tract at CCKS 2019. CS.arxiv 2020-03-08
366 Knowledge Graphs and Knowledge Networks: The Story in Brief AMIT SHETH et. al. This article is an attempt to summarize the journey of KG for AI. CS.arxiv 2020-03-07
367 Uncovering Hidden Semantics of Set Information in Knowledge Bases SHRESTHA GHOSH et. al. In this paper we aim at uncovering this hidden knowledge. CS.arxiv 2020-03-06
368 Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning ELAD AMRANI et. al. In this work, we show that the problem of noise estimation for multimodal data can be reduced to a multimodal density estimation task. CS.arxiv 2020-03-06
369 Practical Annotation Strategies for Question Answering Datasets BERNHARD KRATZWALD et. al. To provide a remedy for practitioners, our objective is to develop heuristic rules for annotating a subset of questions, so that the annotation cost is reduced while maintaining both in- and out-of-domain performance. CS.arxiv 2020-03-06
370 Natural Language QA Approaches using Reasoning with External Knowledge CHITTA BARAL et. al. In this paper, we present a survey of the recent work on them. CS.arxiv 2020-03-06
371 Zero-Shot Cross-Lingual Transfer with Meta Learning FARHAD NOORALAHZADEH et. al. We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first. CS.arxiv 2020-03-05
372 Talking-Heads Attention NOAM SHAZEER et. al. We introduce talking-heads attention – a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the softmax operation.While inserting only a small number of additional parameters and a moderate amount of additionalcomputation, talking-heads attention leads to better perplexities on masked language modeling tasks, aswell as better quality when transfer-learning to language comprehension and question answering tasks. CS.arxiv 2020-03-05
373 A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection DANIELE BONADIMAN et. al. In this paper, we argue that by exploiting the intrinsic structure of the original rank together with an effective word-relatedness encoder, we can achieve competitive results with respect to the state of the art while retaining high efficiency. CS.arxiv 2020-03-04
374 A Study on Multimodal and Interactive Explanations for Visual Question Answering KAMRAN ALIPOUR et. al. We evaluate multimodal explanations in the setting of a Visual Question Answering (VQA) task, by asking users to predict the response accuracy of a VQA agent with and without explanations. CS.arxiv 2020-03-01
375 DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding YUYU ZHANG et. al. To address the efficiency problem, we propose DC-BERT, a decoupled contextual encoding framework that has dual BERT models: an online BERT which encodes the question only once, and an offline BERT which pre-encodes all the documents and caches their encodings. CS.arxiv 2020-02-28
376 AraBERT: Transformer-based Model for Arabic Language Understanding WISSAM ANTOUN et. al. In this paper, we pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language. CS.arxiv 2020-02-28
377 Generating Followup Questions for Interpretable Multi-hop Question Answering CHRISTOPHER MALON et. al. We propose a framework for answering open domain multi-hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretrained single-hop answer extractor. CS.arxiv 2020-02-27
378 Unshuffling Data for Improved Generalization DAMIEN TENEY et. al. We show that the common practice of mixing and shuffling training examples when training deep neural networks is not optimal. CS.arxiv 2020-02-26
379 Multimodal Transformer with Pointer Network for the DSTC8 AVSD Challenge HUNG LE et. al. In this work, we describe our submission to the AVSD track of the 8th Dialogue System Technology Challenge. CS.arxiv 2020-02-25
380 End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings ROSTISLAV NEDELCHEV et. al. Based on this idea, we propose the first end-to-end neural network approach that employs KG as well as word embeddings to perform joint relation and entity classification of simple questions while implicitly performing entity disambiguation with the help of a novel gating mechanism. CS.arxiv 2020-02-25
381 Exploring BERT Parameter Efficiency on the Stanford Question Answering Dataset v2.0 ERIC HULBURD In this paper we explore the parameter efficiency of BERT arXiv:1810.04805 on version 2.0 of the Stanford Question Answering dataset (SQuAD2.0). CS.arxiv 2020-02-25
382 Predicting Subjective Features from Questions on QA Websites using BERT ISSA ANNAMORADNEJAD et. al. Therefore, with the overall goal of providing solutions for automating moderation actions in Q&A websites, we aim to provide a model to predict 20 quality or subjective aspects of questions in QA websites. CS.arxiv 2020-02-24
383 FONDUE: A Framework for Node Disambiguation Using Network Embeddings AHMAD MEL et. al. This can be valuable for a range of purposes from the study of information diffusion to bibliographic analysis, bioinformatics research, and question-answering. CS.arxiv 2020-02-24
384 Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? YIXUAN TANG et. al. In this paper, we investigate whether top-performing models for multi-hop questions understand the underlying sub-questions like humans. CS.arxiv 2020-02-23
385 Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods RUSSA BISWAS et. al. This paper provides a theoretical analysis and comparison of the state-of-the-art alignment methods between two embedding spaces representing entity-entity and entity-word. CS.arxiv 2020-02-21
387 Training Question Answering Models From Synthetic Data RAUL PURI et. al. This work aims to narrow this gap by taking advantage of large language models and explores several factors such as model size, quality of pretrained models, scale of data synthesized, and algorithmic choices. CS.arxiv 2020-02-21
388 VQA-LOL: Visual Question Answering under the Lens of Logic TEJAS GOKHALE et. al. In this paper, we investigate whether visual question answering (VQA) systems trained to answer a question about an image, are able to answer the logical composition of multiple such questions. CS.arxiv 2020-02-19
389 Interactive Natural Language-based Person Search VIKRAM SHREE et. al. In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions. CS.arxiv 2020-02-19
390 Neural Relation Prediction for Simple Question Answering over Knowledge Graph AMIN ABOLGHASEMI et. al. In this paper, we propose an instance-based method to capture the underlying relation of question and to this aim, we detect matching paraphrases of a new question which share the same relation, and their corresponding relation is selected as our prediction. CS.arxiv 2020-02-18
391 SentenceMIM: A Latent Variable Language Model MICHA LIVNE et. al. We introduce sentenceMIM, a probabilistic auto-encoder for language modelling, trained with Mutual Information Machine (MIM) learning. CS.arxiv 2020-02-18
392 CQ-VQA: Visual Question Answering on Categorized Questions AAKANSHA MISHRA et. al. This paper proposes CQ-VQA, a novel 2-level hierarchical but end-to-end model to solve the task of visual question answering (VQA). CS.arxiv 2020-02-17
393 Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey ZAHRA ABBASIANTAEB et. al. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both traditional information retrieval perspective, and more recent deep neural network perspective. We also introduce well-known datasets for the task and present available results from the literature to have a comparison between different techniques. CS.arxiv 2020-02-16
394 FQuAD: French Question Answering Dataset MARTIN D’HOFFSCHMIDT et. al. In the present work, we introduce the French Question Answering Dataset (FQuAD). CS.arxiv 2020-02-14
395 Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks CARLOS ASPILLAGA et. al. For that reason, this work evaluates three Transformer-based models (RoBERTa, XLNet, and BERT) in Natural Language Inference (NLI) and Question Answering (QA) tasks to know if they are more robust or if they have the same flaws as their predecessors. CS.arxiv 2020-02-14
396 Sparse and Structured Visual Attention PEDRO HENRIQUE MARTINS et. al. In this paper, to better link the image structure with the text, we replace the traditional softmax attention mechanism with two alternative sparsity-promoting transformations: sparsemax, which is able to select the relevant regions only (assigning zero weight to the rest), and a newly proposed Total-Variation Sparse Attention (TVmax), which further encourages the joint selection of adjacent spatial locations. CS.arxiv 2020-02-13
397 Component Analysis for Visual Question Answering Architectures CAMILA KOLLING et. al. The main goal of this paper is to provide a comprehensive analysis regarding the impact of each component in VQA models. CS.arxiv 2020-02-12
398 Outlier Guided Optimization of Abdominal Segmentation YUCHEN XU et. al. Herein, we propose a single-pass active learning method through human quality assurance (QA). CS.arxiv 2020-02-10
399 HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention QIMING BAO et. al. This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model. CS.arxiv 2020-02-08
400 One-Shot Learning for Long-Tail Visual Relation Detection WEITAO WANG et. al. With this in mind, we designed a novel model for visual relation detection that works in one-shot settings. CS.aaai 2020-02-07
401 JEC-QA: A Legal-Domain Question Answering Dataset HAOXI ZHONG et. al. We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China. CS.aaai 2020-02-07
402 Description Based Text Classification with Reinforcement Learning DUO CHAI et. al. Inspired by the current trend of formalizing NLP problems as question answering tasks, we propose a new framework for text classification, in which each category label is associated with a category description. CS.arxiv 2020-02-07
403 Evaluating Commonsense in Pre-Trained Language Models XUHUI ZHOU et. al. We study the commonsense ability of GPT, BERT, XLNet, and RoBERTa by testing them on seven challenging benchmarks, finding that language modeling and its variants are effective objectives for promoting models’ commonsense ability while bi-directional context and larger training set are bonuses. We release a test set, named CATs publicly, for future research. CS.aaai 2020-02-07
404 ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion BINGNING WANG et. al. This paper presents the ReCO, a human-curated Chinese Reading Comprehension dataset on Opinion. CS.aaai 2020-02-07
405 ManyModalQA: Modality Disambiguation and QA over Diverse Inputs DARRYL HANNAN et. al. We present a new multimodal question answering challenge, ManyModalQA, in which an agent must answer a question by considering three distinct modalities: text, images, and tables. We collect our data by scraping Wikipedia and then utilize crowdsourcing to collect question-answer pairs. CS.aaai 2020-02-07
406 Differentiable Reasoning on Large Knowledge Bases and Natural Language PASQUALE MINERVINI et. al. We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models. CS.aaai 2020-02-07
407 KnowIT VQA: Answering Knowledge-Based Questions about Videos NOA GARCIA et. al. We propose a novel video understanding task by fusing knowledge-based and video question answering. CS.aaai 2020-02-07
408 Reasoning with Heterogeneous Graph Alignment for Video Question Answering PIN JIANG et. al. We propose a deep heterogeneous graph alignment network over the video shots and question words. CS.aaai 2020-02-07
409 Translating Web Search Queries into Natural Language Questions ADARSH KUMAR et. al. In this paper, we are proposing a method to generate well-formed natural language question from a given keyword-based query, which has the same question intent as the query. CS.arxiv 2020-02-07
410 DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue XIAOZE JIANG et. al. In this research, we propose a novel model to depict an image from both visual and semantic perspectives. CS.aaai 2020-02-07
411 End-to-End Argumentation Knowledge Graph Construction KHALID AL-KHATIB et. al. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model’s elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. CS.aaai 2020-02-07
412 V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices DAMIEN TENEY et. al. We propose a new large-scale benchmark to evaluates abstract reasoning over real visual data. CS.aaai 2020-02-07
413 Divide and Conquer: Question-Guided Spatio-Temporal Contextual Attention for Video Question Answering JIANWEN JIANG et. al. To tackle this problem, we propose a Question-Guided Spatio-Temporal Contextual Attention Network (QueST) method. CS.aaai 2020-02-07
414 Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension QIYU REN et. al. In this paper, we present MG-MRC, a novel approach for multi-passage MRC via multi-task learning with generative adversarial training. CS.aaai 2020-02-07
415 QASC: A Dataset for Question Answering via Sentence Composition TUSHAR KHOT et. al. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. CS.aaai 2020-02-07
416 What Do You Mean ‘Why?’: Resolving Sluices in Conversations VICTOR PETRÉN BACH HANSEN et. al. This paper introduces the novel ellipsis resolution task of resolving such one-word questions, referred to as sluices in linguistics. We present a crowd-sourced dataset containing annotations of sluices from over 4,000 dialogues collected from conversational QA datasets, as well as a series of strong baseline architectures. CS.aaai 2020-02-07
417 Segment-Then-Rank: Non-Factoid Question Answering on Instructional Videos KYUNGJAE LEE et. al. Motivated by this, we propose a two-stage model: (a) multimodal segmentation of video into span candidates and (b) length-adaptive ranking of the candidates to the question. CS.aaai 2020-02-07
418 Revisiting Bilinear Pooling: A Coding Perspective ZHI GAO et. al. In this paper, we prove that bilinear pooling is indeed a similarity-based coding-pooling formulation. CS.aaai 2020-02-07
419 Capturing Greater Context for Question Generation LUU ANH TUAN et. al. Our goal is to incorporate interactions across multiple sentences to generate realistic questions for long documents. CS.aaai 2020-02-07
420 The Value of Paraphrase for Knowledge Base Predicates BINGCONG XUE et. al. This paper shows a full process of collecting large-scale and high-quality paraphrase dictionaries for predicates in knowledge bases, which takes advantage of existing datasets and combines the technologies of machine mining and crowdsourcing. CS.aaai 2020-02-07
421 Attentive User-Engaged Adversarial Neural Network for Community Question Answering YUEXIANG XIE et. al. We present an Attentive User-engaged Adversarial Neural Network (AUANN), which interactively learns the context information of questions and answers, and enhances user engagement with the CQA task. CS.aaai 2020-02-07
422 Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA BADRI PATRO et. al. In this paper, we aim to obtain improved attention for a visual question answering (VQA) task. CS.aaai 2020-02-07
423 Federated Learning for Vision-and-Language Grounding Problems FENGLIN LIU et. al. Inspired by the recent success of federated learning, we propose a federated learning framework to obtain various types of image representations from different tasks, which are then fused together to form fine-grained image representations. CS.aaai 2020-02-07
424 Unified Vision-Language Pre-Training for Image Captioning and VQA LUOWEI ZHOU et. al. This paper presents a unified Vision-Language Pre-training (VLP) model. CS.aaai 2020-02-07
425 Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models ARINDAM MITRA et. al. As part of this work, we have developed two datasets that help mitigate such issues and make the systems better at understanding the notion of “entities” and “roles”. CS.aaai 2020-02-07
426 An Empirical Study of Content Understanding in Conversational Question Answering TING-RUI CHIANG et. al. 2) Do the models well utilize the conversation content when answering questions? CS.aaai 2020-02-07
427 On the Generation of Medical Question-Answer Pairs SHENG SHEN et. al. With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. CS.aaai 2020-02-07
428 Overcoming Language Priors in VQA via Decomposed Linguistic Representations CHENCHEN JING et. al. In this paper, we present a novel method of language attention-based VQA that learns decomposed linguistic representations of questions and utilizes the representations to infer answers for overcoming language priors. CS.aaai 2020-02-07
429 Multi-Speaker Video Dialog with Frame-Level Temporal Localization QIANG WANG et. al. In this paper, we introduce a novel task of Multi-Speaker Video Dialog with frame-level Temporal Localization (MSVD-TL) to make video dialog systems more applicable. CS.aaai 2020-02-07
430 Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation MAHMOUD KHADEMI et. al. We propose a new algorithm, called Deep Generative Probabilistic Graph Neural Networks (DG-PGNN), to generate a scene graph for an image. CS.aaai 2020-02-07
431 PIQA: Reasoning about Physical Commonsense in Natural Language YONATAN BISK et. al. In this paper, we introduce the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. CS.aaai 2020-02-07
432 Location-Aware Graph Convolutional Networks for Video Question Answering DENG HUANG et. al. In this work, we propose to represent the contents in the video as a location-aware graph by incorporating the location information of an object into the graph construction. CS.aaai 2020-02-07
433 Multi-Question Learning for Visual Question Answering CHENYI LEI et. al. To explore these relations, we propose a new paradigm for VQA termed Multi-Question Learning (MQL). CS.aaai 2020-02-07
434 Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering ZIHAN XU et. al. In this work, a novel knowledge and cross-pair pattern guided semantic matching system (KCG) is proposed, which considers both knowledge and pattern conditions for QA. CS.aaai 2020-02-07
435 Generating Well-Formed Answers by Machine Reading with Stochastic Selector Networks BIN BI et. al. For the generative QA task, we introduce a new neural architecture, LatentQA, in which a novel stochastic selector network composes a well-formed answer with words selected from the question, the paragraph and the global vocabulary, based on a sequence of discrete latent variables. CS.aaai 2020-02-07
436 Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data KYOUNG-WOON ON et. al. Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video. CS.aaai 2020-02-07
437 Hashing Based Answer Selection DONG XU et. al. In this paper, we propose a novel method, called hashing based answer selection (HAS), to tackle this problem. CS.aaai 2020-02-07
438 CFGNN: Cross Flow Graph Neural Networks for Question Answering on Complex Tables XUANYU ZHANG To leverage more context information flow comprehensively, we propose novel cross flow graph neural networks in this paper. CS.aaai 2020-02-07
439 Improving Knowledge-Aware Dialogue Generation via Knowledge Base Question Answering JIAN WANG et. al. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. CS.aaai 2020-02-07
440 An Iterative Polishing Framework Based on Quality Aware Masked Language Model for Chinese Poetry Generation LIMING DENG et. al. In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation. CS.aaai 2020-02-07
441 Re-Attention for Visual Question Answering WENYA GUO et. al. In this paper, to utilize the information in answer, we propose a re-attention framework for the VQA task. CS.aaai 2020-02-07
442 Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering YANG DENG et. al. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. CS.aaai 2020-02-07
443 Masking Orchestration: Multi-Task Pretraining for Multi-Role Dialogue Representation Learning TIANYI WANG et. al. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. CS.aaai 2020-02-07
444 Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks ANNA ROGERS et. al. We present QuAIL, the first RC dataset to combine text-based, world knowledge and unanswerable questions, and to provide question type annotation that would enable diagnostics of the reasoning strategies by a given QA system. CS.aaai 2020-02-07
445 Learning to Interactively Learn and Assist MARK WOODWARD et. al. In this paper, we propose such interactive learning as an alternative to reward or demonstration-driven learning. CS.aaai 2020-02-07
446 MMM: Multi-Stage Multi-Task Learning for Multi-Choice Reading Comprehension DI JIN et. al. We introduce MMM, a Multi-stage Multi-task learning framework for Multi-choice reading comprehension. CS.aaai 2020-02-07
447 TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection SIDDHANT GARG et. al. We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. We built a large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. CS.aaai 2020-02-07
448 Translucent Answer Predictions in Multi-Hop Reading Comprehension G P SHRIVATSA BHARGAV et. al. In this paper, we propose a novel deep neural architecture, called TAP (Translucent Answer Prediction), to identify answers and evidence (in the form of supporting facts) in an RCQA task requiring multi-hop reasoning. CS.aaai 2020-02-07
449 Generating Scientific Question Answering Corpora from Q&A forums ANDRE LAMURIAS et. al. We present a method to automatically extract question-article pairs from Q&A web forums, which can be used for document retrieval and QA tasks. We generated the SciQA corpus by applying our framework to three forums, obtaining 5,432 questions and 10,208 question-article pairs. CS.arxiv 2020-02-06
450 UNCC Biomedical Semantic Question Answering Systems. BioASQ: Task-7B, Phase-B SAI KRISHNA TELUKUNTLA et. al. In this paper, we detail our submission to the 2019, 7th year, BioASQ competition. CS.arxiv 2020-02-05
451 Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review PENGCHENG ZHANG et. al. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. CS.arxiv 2020-02-05
452 K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters RUIZE WANG et. al. To address this, we propose K-Adapter, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion. CS.arxiv 2020-02-05
453 Web Table Extraction, Retrieval and Augmentation: A Survey SHUO ZHANG et. al. The objective of this survey is to synthesize and present two decades of research on web tables. CS.arxiv 2020-02-01
454 Break It Down: A Question Understanding Benchmark TOMER WOLFSON et. al. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. CS.arxiv 2020-01-31
455 Teaching Machines to Converse JIWEI LI This dissertation attempts to tackle these challenges: Contributions are two-fold: (1) we address new challenges presented by neural network models in open-domain dialogue generation systems; (2) we develop interactive question-answering dialogue systems by (a) giving the agent the ability to ask questions and (b) training a conversation agent through interactions with humans in an online fashion, where a bot improves through communicating with humans and learning from the mistakes that it makes. CS.arxiv 2020-01-31
456 Augmenting Visual Question Answering with Semantic Frame Information in a Multitask Learning Approach MEHRDAD ALIZADEH et. al. Several deep neural network approaches have been proposed to model the task in an end-to-end fashion. CS.arxiv 2020-01-31
457 Pretrained Transformers for Simple Question Answering over Knowledge Graphs D. LUKOVNIKOV et. al. In this work, we investigate how well BERT performs on SimpleQuestions and provide an evaluation of both BERT and BiLSTM-based models in datasparse scenarios. CS.arxiv 2020-01-31
458 Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks LENA SCHMIDT et. al. All models in this paper were created with the aim to support systematic review (semi)automation. CS.arxiv 2020-01-30
459 MEMO: A Deep Network for Flexible Combination of Episodic Memories ANDREA BANINO et. al. Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning. CS.arxiv 2020-01-29
460 The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability MEHDI ALI et. al. Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability. CS.arxiv 2020-01-28
461 Intent Classification in Question-Answering Using LSTM Architectures GIOVANNI DI GENNARO et. al. Through the use of an LSTM network, we show how this type of classification can be approached effectively and efficiently, and how it can be properly used within a basic prototype responder. CS.arxiv 2020-01-25
462 Robust Explanations for Visual Question Answering BADRI N. PATRO et. al. In this paper, we propose a method to obtain robust explanations for visual question answering(VQA) that correlate well with the answers. CS.arxiv 2020-01-23
463 Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models MOSHIUR R. FARAZI et. al. In this work, we systematically study the trade-off between the model complexity and the performance on the VQA task. CS.arxiv 2020-01-20
464 Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model SILEI XU et. al. This paper proposes Schema2QA, an open-source toolkit that can build a Q&A skill from a database schema, requiring just a few manual annotations on each field. CS.arxiv 2020-01-15
465 Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features ANDRES MAFLA et. al. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. CS.arxiv 2020-01-14
466 Negative Statements Considered Useful HIBA ARNAOUT et. al. We introduce two approaches towards compiling negative statements. CS.arxiv 2020-01-13
467 MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding GEONDO PARK et. al. Thus, in addition to the joint embedding space, we propose a novel multi-head self-attention network to capture various components of visual and textual data by attending to important parts in data. CS.arxiv 2020-01-11
468 Machine Learning Approaches for Amharic Parts-of-speech Tagging IBRAHIM GASHAW et. al. The aim of this work is to improve POS tagging performance for the Amharic language, which was never above 91%. CS.arxiv 2020-01-10
469 Visual Question Answering on 360° Images SHIH-HAN CHOU et. al. In this work, we introduce VQA 360, a novel task of visual question answering on 360 images. To address this problem, we collect the first VQA 360 dataset, containing around 17,000 real-world image-question-answer triplets for a variety of question types. CS.arxiv 2020-01-10
470 Open Domain Question Answering Using Web Tables KAUSHIK CHAKRABARTI et. al. In this paper, we develop an open-domain QA approach using web tables that works for both factoid and non-factoid queries. CS.arxiv 2020-01-09
471 Managing Device Lifecycle: Reconfigurable Constrained Codes for M/T/Q/P-LC Flash Memories AHMED HAREEDY et. al. Recently, we introduced binary symmetric lexicographically-ordered constrained codes (LOCO codes) for data storage and data transmission. CS.arxiv 2020-01-07
472 Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering LEI SHI et. al. Extensive ablation study has been carried out to testify the influence of each proposed module in this study. CS.arxiv 2020-01-02
473 Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks JEFFREY O ZHANG et. al. In this paper, we propose a straightforward alternative: side-tuning. CS.arxiv 2019-12-31
474 What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge KYLE RICHARDSON et. al. To investigate this, we introduce several new challenge tasks that probe whether state-of-the-art QA models have general knowledge about word definitions and general taxonomic reasoning, both of which are fundamental to more complex forms of reasoning and are widespread in benchmark datasets. CS.arxiv 2019-12-31
475 Natural Questions: A Benchmark for Question Answering Research TOM KWIATKOWSKI et. al. We present the Natural Questions corpus, a question answering data set. CS.tacl 2019-12-30
476 CoQA: A Conversational Question Answering Challenge SIVA REDDY et. al. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. CS.tacl 2019-12-30
477 Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs AMRITA SAHA et. al. We present Complex Imperative Program Induction from Terminal Rewards (CIPITR), an advanced neural programmer that mitigates reward sparsity with auxiliary rewards, and restricts the program space to semantically correct programs using high-level constraints, KB schema, and inferred answer type. CS.tacl 2019-12-30
478 The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries ATEF CHAUDHURY et. al. In this paper, we introduce the Shmoop Corpus: a dataset of 231 stories that are paired with detailed multi-paragraph summaries for each individual chapter (7,234 chapters), where the summary is chronologically aligned with respect to the story chapter. From the corpus, we construct a set of common NLP tasks, including Cloze-form question answering and a simplified form of abstractive summarization, as benchmarks for reading comprehension on stories. CS.arxiv 2019-12-30
479 Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering ERIC WALLACE et. al. We propose human- in-the-loop adversarial generation, where human authors are guided to break models. CS.tacl 2019-12-30
480 Vision and Language: from Visual Perception to Content Creation TAO MEI et. al. This paper reviews the recent advances along these two dimensions: vision to language and language to vision. CS.arxiv 2019-12-26
481 Learning to Answer Ambiguous Questions with Knowledge Graph YIKAI ZHU et. al. In this paper, we present a new way to utilize the dataset that takes into account the existence of ambiguous questions. CS.arxiv 2019-12-25
482 Efficient Transformer for Mobile Applications ZHANGHAO WU* et. al. In this paper, we investigate the mobile setting (under 500M Mult-Adds) for NLP tasks to facilitate the deployment on the edge devices. CS.iclr 2019-12-21
483 Reducing Transformer Depth on Demand with Structured Dropout ANGELA FAN et. al. Layerdrop, a form of structured dropout that allows you to train one model at training time and prune to any desired depth at test time. You can also CS.iclr 2019-12-21
484 MEMO: A Deep Network for Flexible Combination of Episodic Memories ANDREA BANINO et. al. A memory architecture that support inferential reasoning. CS.iclr 2019-12-21
485 AdvCodec: Towards A Unified Framework for Adversarial Text Generation BOXIN WANG et. al. The main contribution of this paper is to propose a general targeted attack framework AdvCodec for adversarial text generation which addresses the challenge of discrete input space and is easily adapted to general natural language processing (NLP) tasks. CS.arxiv 2019-12-21
486 Learning from Explanations with Neural Module Execution Tree YUJIA QIN et. al. In this paper, we propose a novel Neural Modular Execution Tree (NMET) framework for augmenting sequence classification with NL explanations. CS.iclr 2019-12-21
487 Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model WENHAN XIONG et. al. In this work, we further investigate the extent to which pretrained models such as BERT capture knowledge using a zero-shot fact completion task. CS.iclr 2019-12-21
488 Transformer-XH: Multi-hop question answering with eXtra Hop attention CHEN ZHAO et. al. We present Transformer-XH, which upgrades Transformer with eXtra Hop attentions to intrinsically model structured texts in a data driven way. It leads to a simpler yet state-of-the-art multi-hop QA system. CS.iclr 2019-12-21
489 Abductive Commonsense Reasoning CHANDRA BHAGAVATULA et. al. We present the first study that investigates the viability of language-based abductive reasoning. We introduce a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations. CS.iclr 2019-12-21
490 StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding WEI WANG et. al. Inspired by the linearization exploration work of Elman, we extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. CS.iclr 2019-12-21
491 NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension SEOHYUN BACK et. al. We propose a neural question requirement inspection model called NeurQuRI that extracts a list of conditions from the question, each of which should be satisfied by the candidate answer generated by an MRC model. CS.iclr 2019-12-21
492 Measuring Compositional Generalization: A Comprehensive Method on Realistic Data DANIEL KEYSERS et. al. Benchmark and method to measure compositional generalization by maximizing divergence of compound frequency at small divergence of atom frequency. CS.iclr 2019-12-21
493 Measuring Compositional Generalization: A Comprehensive Method on Realistic Data DANIEL KEYSERS et. al. We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks. CS.arxiv 2019-12-20
494 Deep Exemplar Networks for VQA and VQG BADRI N. PATRO et. al. In this paper, we consider the problem of solving semantic tasks such as `Visual Question Answering’ (VQA), where one aims to answers related to an image and `Visual Question Generation’ (VQG), where one aims to generate a natural question pertaining to an image. CS.arxiv 2019-12-19
495 Knowledge-Enhanced Attentive Learning for Answer Selection in Community Question Answering Systems FENGSHI JING et. al. We propose the Knowledge-enhanced Attentive Answer Selection (KAAS) model, which enhances the performance through (a) considering both the expertise and the authority of the answerer; (b) utilizing the human-labeled tags, the taxonomy of the tags, and the votes as the domain knowledge to infer the expertise of the answer; (c) using matrix decomposition of the social network (formed by following-relationship) to infer the authority of the answerer and incorporating such information in the process of evaluating the similarity between segments. CS.arxiv 2019-12-17
496 Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science HUICHEN YANG et. al. This paper describes a machine learning and data science pipeline for structured information extraction from documents, implemented as a suite of open-source tools and extensions to existing tools. CS.arxiv 2019-12-16
497 Knowledge-based Conversational Search SVITLANA VAKULENKO In this thesis, we lay foundations for designing conversational search systems by analyzing the requirements and proposing concrete solutions for automating some of the basic components and tasks that such systems should support. CS.arxiv 2019-12-14
498 AliMe KBQA: Question Answering over Structured Knowledge for E-commerce Customer Service FENG-LIN LI et. al. In this paper, we present AliMe-KBQA, a bold attempt to apply KBQA in the E-commerce customer service field. CS.arxiv 2019-12-11
499 Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering CASIMIRO PIO CARRINO et. al. In this work, we develop the Translate Align Retrieve (TAR) method to automatically translate the Stanford Question Answering Dataset (SQuAD) v1.1 to Spanish. CS.arxiv 2019-12-11
500 Efficient crowdsourcing of crowd-generated microtasks ABIGAIL HOTALING et. al. In this paper, we introduce *cost forecasting* as a means for a crowdsourcer to use efficient crowdsourcing algorithms with a growing set of microtasks. CS.arxiv 2019-12-10
501 Medication Regimen Extraction From Medical Conversations SAI P. SELVARAJ et. al. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation. CS.arxiv 2019-12-10
502 Neural Module Networks for Reasoning over Text NITISH GUPTA et. al. We extend NMNs by: (a) introducing modules that reason over a paragraph of text, performing symbolic reasoning (such as arithmetic, sorting, counting) over numbers and dates in a probabilistic and differentiable manner; and (b) proposing an unsupervised auxiliary loss to help extract arguments associated with the events in text. CS.arxiv 2019-12-10
503 AI2D-RST: A multimodal corpus of 1000 primary school science diagrams TUOMO HIIPPALA et. al. This article introduces AI2D-RST, a multimodal corpus of 1000 English-language diagrams that represent topics in primary school natural sciences, such as food webs, life cycles, moon phases and human physiology. CS.arxiv 2019-12-09
504 Exploring the Ideal Depth of Neural Network when Predicting Question Deletion on Community Question Answering SOUVICK GHOSH et. al. In this paper, we have explored how the depth of the neural network influences the accuracy of prediction of deleted questions in question-answering forums. CS.arxiv 2019-12-07
505 Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks CORENTIN KERVADEC et. al. In this paper we claim that these State-Of-The-Art (SOTA) approaches perform reasonably well in structuring information inside a single modality but, despite their impressive performances , they tend to struggle to identify fine-grained inter-modality relationships. CS.arxiv 2019-12-06
506 Machine Translation Evaluation Meets Community Question Answering FRANCISCO GUZMÁN et. al. We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering. CS.arxiv 2019-12-06
507 Cooperative Reasoning on Knowledge Graph and Corpus: A Multi-agentReinforcement Learning Approach YUNAN ZHANG et. al. In this paper, we propose a model that leverages the text corpus to cure such limitations, either the explicit or implicit missing links. CS.arxiv 2019-12-04
508 An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering SHAYNE LONGPRE et. al. We find a simple negative sampling technique to be particularly effective, even though it is typically used for datasets that include unanswerable questions, such as SQuAD 2.0. CS.arxiv 2019-12-04
509 Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline VISHVAK MURAHARI et. al. Instead, we present an approach to leverage pretraining on related vision-language datasets before transferring to visual dialog. CS.arxiv 2019-12-04
510 Deep Bayesian Active Learning for Multiple Correct Outputs KHALED JEDOUI et. al. In this paper, we propose a new paradigm that estimates uncertainty in the model’s internal hidden space instead of the model’s output space. CS.arxiv 2019-12-02
511 Unsupervised Inflection Generation Using Neural Language Modeling OCTAVIA-MARIA SULEA et. al. In this paper we investigate the extent to which these new unsupervised or generative techniques can serve to alleviate the type-token ratio disparity in morphologically rich languages. CS.arxiv 2019-12-02
512 TutorialVQA: Question Answering Dataset for Tutorial Videos ANTHONY COLAS et. al. To adequately address this, We propose a new question answering task on instructional videos, because of their verbose and narrative nature. CS.arxiv 2019-12-02
513 A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique MD. HASIBUR RAHMAN et. al. We present a two stage QC system for Bengali. CS.arxiv 2019-11-29
514 Transfer Learning in Visual and Relational Reasoning T. S. JAYRAM et. al. In this work, we formalize these unique aspects of transfer learning and propose a theoretical framework for visual reasoning, exemplified by the well-established CLEVR and COG datasets. CS.arxiv 2019-11-26
515 Label Dependent Deep Variational Paraphrase Generation SIAMAK SHAKERI et. al. In this article, we propose a deep variational model to generate paraphrases conditioned on a label that specifies whether the paraphrases are semantically related or not. CS.arxiv 2019-11-26
516 A Vietnamese Text-Based Conversational Agent DAI QUOC NGUYEN et. al. This paper introduces a Vietnamese text-based conversational agent architecture on specific knowledge domain which is integrated in a question answering system. CS.arxiv 2019-11-26
517 SemEval-2015 Task 3: Answer Selection in Community Question Answering PRESLAV NAKOV et. al. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. CS.arxiv 2019-11-26
518 Unsupervised Domain Adaptation of Language Models for Reading Comprehension KOSUKE NISHIDA et. al. To solve the UDARC problem, we provide two domain adaptation models. CS.arxiv 2019-11-25
519 Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering AKARI ASAI et. al. This paper introduces a new graph-based recurrent retrieval approach that learns to retrieve reasoning paths over the Wikipedia graph to answer multi-hop open-domain questions. CS.arxiv 2019-11-24
520 ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition HANNAH SMITH et. al. In this work we present ScienceExamCER, a densely-labeled semantic classification corpus of 133k mentions in the science exam domain where nearly all (96%) of content words have been annotated with one or more fine-grained semantic class labels including taxonomic groups, meronym groups, verb/action groups, properties and values, and synonyms. CS.arxiv 2019-11-23
521 Unsupervised Keyword Extraction for Full-sentence VQA KOHEI UEHARA et. al. In this study, we envision a new VQA task in natural situations, where the answers would more likely to be sentences, rather than single words. CS.arxiv 2019-11-23
522 The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service MENG CHEN et. al. In this paper, we construct a large-scale real scenario Chinese E-commerce conversation corpus, JDDC, with more than 1 million multi-turn dialogues, 20 million utterances, and 150 million words. CS.arxiv 2019-11-22
523 Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation EDWIN SIMPSON et. al. To address the lack of user-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. CS.arxiv 2019-11-22
524 Temporal Reasoning via Audio Question Answering HAYTHAM M. FAYEK et. al. In this paper, we use the task of Audio Question Answering (AQA) to study the temporal reasoning abilities of machine learning models. CS.arxiv 2019-11-21
525 Paraphrasing with Large Language Models SAM WITTEVEEN et. al. We present a useful technique for using a large language model to perform the task of paraphrasing on a variety of texts and subjects. CS.arxiv 2019-11-21
526 What Do You Mean `Why?’: Resolving Sluices in Conversations VICTOR PETRÉN BACH HANSEN et. al. This paper introduces the novel ellipsis resolution task of resolving such one-word questions, referred to as sluices in linguistics. We present a crowd-sourced dataset containing annotations of sluices from over 4,000 dialogues collected from conversational QA datasets, as well as a series of strong baseline architectures. CS.arxiv 2019-11-21
527 Improving Conditioning in Context-Aware Sequence to Sequence Models XINYI WANG et. al. In this work, we focus on cases where generation is conditioned on both a short query and a long context, such as abstractive question answering or document-level translation. CS.arxiv 2019-11-21
528 Global Thread-Level Inference for Comment Classification in Community Question Answering SHAFIQ JOTY et. al. More specifically, we exploit the relations between pairs of comments at any distance in the thread, which we incorporate in a graph-cut and in an ILP frameworks. CS.arxiv 2019-11-20
529 SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings TODOR MIHAYLOV et. al. We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering. CS.arxiv 2019-11-20
530 Unsupervised Natural Question Answering with a Small Model MARTIN ANDREWS et. al. The contribution of this work is that the methods presented here rely on unsupervised learning techniques, complementing the unsupervised training of the Language Model. CS.arxiv 2019-11-19
531 Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA BADRI N. PATRO et. al. In this paper, we aim to obtain improved attention for a visual question answering (VQA) task. CS.arxiv 2019-11-19
532 Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems QIANG HUANG et. al. In this paper, we propose a Multi-task Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is employed to depict the relation between sentences, and a multi-task learning model is applied to address both the sentence matching and sentence intent classification problem. CS.arxiv 2019-11-17
533 Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering VIKAS YADAV et. al. We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. CS.arxiv 2019-11-17
534 Neural Shuffle-Exchange Networks – Sequence Processing in O(n log n) Time KARLIS FREIVALDS et. al. We introduce a new Shuffle-Exchange neural network model for sequence to sequence tasks which have O(log n) depth and O(n log n) total complexity. CS.nips 2019-11-15
535 Unified Language Model Pre-training for Natural Language Understanding and Generation LI DONG et. al. This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. CS.nips 2019-11-15
536 Heterogeneous Graph Learning for Visual Commonsense Reasoning WEIJIANG YU et. al. In this paper, we propose a new Heterogeneous Graph Learning (HGL) framework for seamlessly integrating the intra-graph and inter-graph reasoning in order to bridge the vision and language domain. CS.nips 2019-11-15
537 XLNet: Generalized Autoregressive Pretraining for Language Understanding ZHILIN YANG et. al. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. CS.nips 2019-11-15
538 TAB-VCR: Tags and Attributes based VCR Baselines JINGXIANG LIN et. al. Here we show that a much simpler model obtained by ablating and pruning the existing intricate baseline can perform better with half the number of trainable parameters. CS.nips 2019-11-15
539 ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks JIASEN LU et. al. We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. CS.nips 2019-11-15
540 Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations FENGLIN LIU et. al. In this work, we aim at representing an image with a set of integrated visual regions and corresponding textual concepts, reflecting certain semantics. CS.nips 2019-11-15
541 Self-Critical Reasoning for Robust Visual Question Answering JIALIN WU et. al. To address this issue, we introduce a self-critical training objective that ensures that visual explanations of correct answers match the most influential image regions more than other competitive answer candidates. CS.nips 2019-11-15
542 RUBi: Reducing Unimodal Biases for Visual Question Answering REMI CADENE et. al. We propose RUBi, a new learning strategy to reduce biases in any VQA model. CS.nips 2019-11-15
543 Towards Interpretable Reinforcement Learning Using Attention Augmented Agents ALEXANDER MOTT et. al. Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain. CS.nips 2019-11-15
544 Compositional De-Attention Networks YI TAY et. al. This paper proposes a new quasi-attention that is compositional in nature, i.e., learning whether to \textit{add}, \textit{subtract} or \textit{nullify} a certain vector when learning representations. CS.nips 2019-11-15
545 Question-Conditioned Counterfactual Image Generation for VQA JINGJING PAN et. al. In this ongoing work, we propose addressing this shortcoming by learning to generate counterfactual images for a VQA model – i.e. given a question-image pair, we wish to generate a new image such that i) the VQA model outputs a different answer, ii) the new image is minimally different from the original, and iii) the new image is realistic. CS.arxiv 2019-11-14
546 FAQ-based Question Answering via Knowledge Anchors RUOBING XIE et. al. In this paper, we propose a novel Knowledge Anchor based Question Answering (KAQA) framework for FAQ-based QA to better understand questions and retrieve more appropriate answers. CS.arxiv 2019-11-13
547 Neural Duplicate Question Detection without Labeled Training Data ANDREAS RÜCKLÉ et. al. In this work, we propose two novel methods—weak supervision using the title and body of a question, and the automatic generation of duplicate questions—and show that both can achieve improved performances even though they do not require any labeled data. CS.arxiv 2019-11-13
548 Adapting and evaluating a deep learning language model for clinical why-question answering ANDREW WEN et. al. Objectives: To adapt and evaluate a deep learning language model for answering why-questions based on patient-specific clinical text. CS.arxiv 2019-11-13
549 A Survey on Why-Type Question Answering Systems MANVI BREJA et. al. The paper presents a survey on Why-type Question Answering System, details the architecture, the processes involved in the system and suggests further areas of research. CS.arxiv 2019-11-12
550 EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping ABHISHEK AGARWAL et. al. In this paper, we propose an on-the-fly conceptual network model that incorporates educational semantics. CS.arxiv 2019-11-12
551 Meta Answering for Machine Reading BENJAMIN BORSCHINGER et. al. We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment. CS.arxiv 2019-11-11
552 Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation YIMING XU et. al. In this paper, we tackle the above issues by proposing a novel supervised multi-modal domain adaptation method for VQA to learn joint feature embeddings across different domains and modalities. CS.arxiv 2019-11-10
553 Learning to Order Sub-questions for Complex Question Answering YUNAN ZHANG et. al. In this paper, we propose a novel reinforcement learning(RL) approach to answering complex questions that can learn a policy to dynamically decide which sub-question should be answered at each stage of reasoning. CS.arxiv 2019-11-10
554 Multimodal Intelligence: Representation Learning, Information Fusion, and Applications CHAO ZHANG et. al. In this paper, we provide a technical review of available models and learning methods for multimodal intelligence. CS.arxiv 2019-11-10
555 Dynamic Knowledge Graph Construction for Zero-shot Commonsense Question Answering ANTOINE BOSSELUT et. al. In this paper, we present initial studies toward zero-shot commonsense QA by formulating the task as probabilistic inference over dynamically generated commonsense knowledge graphs. CS.arxiv 2019-11-10
556 Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering SEWON MIN et. al. We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article. CS.arxiv 2019-11-10
557 Knowledge Guided Named Entity Recognition for BioMedical Text PRATYAY BANERJEE et. al. In this work, we formulate the NER task as a multi-answer question answering (MAQA) task and provide different knowledge contexts, such as entity types, questions, definitions and definitions with examples. CS.arxiv 2019-11-10
558 Hierarchical Graph Network for Multi-hop Question Answering YUWEI FANG et. al. In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. CS.arxiv 2019-11-09
559 E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT NINA POERNER et. al. We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT’s native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors. CS.arxiv 2019-11-09
560 A Comprehensive Comparison of Machine Learning Based Methods Used in Bengali Question Classification AFRA ANIKA et. al. We present a comprehensive comparison (performance and computational complexity) among some machine learning based approaches used in QC for Bengali language. CS.arxiv 2019-11-08
561 Fast LSTM Inference by Dynamic Decomposition on Cloud Systems Y. YOU ET AL. In this paper we aim to minimize the latency of LSTM inference on cloud systems without losing accuracy. CS.icdm 2019-11-08
562 Are we asking the right questions in MovieQA? BHAVAN JASANI et. al. In this paper, we explore the biases in the MovieQA dataset and propose a strikingly simple model which can exploit them. CS.arxiv 2019-11-08
563 Multi-hop Knowledge Base Question Answering with an Iterative Sequence Matching Model Y. LAN et. al. In this paper, we propose a novel iterative sequence matching model to address several limitations of previous methods for multi-hop KBQA. CS.icdm 2019-11-08
564 Ask to Learn: A Study on Curiosity-driven Question Generation THOMAS SCIALOM et. al. We propose a novel text generation task, namely Curiosity-driven Question Generation. CS.arxiv 2019-11-08
565 Discovering Relevant Reviews for Answering Product-Related Queries S. ZHANG et. al. To address this, we propose two neural models that discover relevant reviews for answering product queries. CS.icdm 2019-11-08
566 Preference Relationship-Based CrossCMN Scheme for Answer Ranking in Community QA Q. CHEN et. al. To improve such problem, we design a novel scheme, named PW-CrossCMN. The scheme ranks the candidate answers by pair-wise approach based on numerous historical documents. CS.icdm 2019-11-08
567 Why Do Masked Neural Language Models Still Need Common Sense Knowledge? SUNJAE KWON et. al. This paper provides empirical but insightful analyses on the pretrained MNLMs with respect to common sense knowledge. CS.arxiv 2019-11-07
568 Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering LI ZHOU et. al. In this paper, we propose to model multi-domain DST as a question answering problem, referred to as Dialogue State Tracking via Question Answering (DSTQA). CS.arxiv 2019-11-07
569 Blockwise Self-Attention for Long Document Understanding JIEZHONG QIU et. al. We present BlockBERT, a lightweight and efficient BERT model that is designed to better modeling long-distance dependencies. CS.arxiv 2019-11-07
570 The TechQA Dataset VITTORIO CASTELLI et. al. We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain. CS.arxiv 2019-11-07
571 Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks TIMOTHY J. HAZEN et. al. This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains. CS.arxiv 2019-11-06
572 Unsupervised Domain Adaptation of Contextual Embeddings for Low-Resource Duplicate Question Detection ALEXANDRE ROCHETTE et. al. In this paper, we apply BERT to DQD and advance it by unsupervised adaptation to StackExchange domains using self-supervised learning. CS.arxiv 2019-11-06
573 A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World GEORGIY PLATONOV et. al. In this work we tackle spatial question answering in a holistic way, using a vision system, speech input and output mediated by an animated avatar, a dialogue system that robustly interprets spatial queries, and a constraint solver that derives answers based on 3-D spatial modeling. CS.arxiv 2019-11-06
574 Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds TASSILO KLEIN et. al. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. CS.arxiv 2019-11-06
575 Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network DEMING YE et. al. In this work, we propose a knowledge-enhanced graph neural network (KGNN), which performs reasoning over multiple paragraphs with entities. CS.arxiv 2019-11-05
576 BAS: An Answer Selection Method Using BERT Language Model JAMSHID MOZAFARI et. al. In this research, we will present the BAS (BERT Answer Selection) that uses the BERT language model to comprehend language. CS.arxiv 2019-11-04
577 Learning from Explanations with Neural Execution Tree ZIQI WANG et. al. In this paper, we propose a novel Neural Execution Tree (NExT) framework to augment training data for text classification using NL explanations. CS.arxiv 2019-11-04
578 Semantically Driven Auto-completion KONSTANTINE ARKOUDAS et. al. We describe the auto-complete problem as it arises in this setting, the novel algorithms that we use to solve it, and report on the quality of the results and the efficiency of our approach. CS.cikm 2019-11-03
579 MRNN: A Multi-Resolution Neural Network with Duplex Attention for Document Retrieval in the Context of Question Answering TOLGAHAN CAKALOGLU et. al. In this paper, we devise a multi-resolution neural network(MRNN) to leverage the whole hierarchy of representations for document retrieval. CS.arxiv 2019-11-03
580 Message Passing for Complex Question Answering over Knowledge Graphs SVITLANA VAKULENKO et. al. We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers. CS.cikm 2019-11-03
581 MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data YANG DENG et. al. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. CS.cikm 2019-11-03
582 How Does BERT Answer Questions?: A Layer-Wise Analysis of Transformer Representations BETTY VAN AKEN et. al. In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT’s hidden states. CS.cikm 2019-11-03
583 Scene Graph based Image Retrieval — A case study on the CLEVR Dataset SAHANA RAMNATH et. al. Motivated by this, we propose a neural-symbolic approach for a one-shot retrieval of images from a large scale catalog, given the caption description. CS.arxiv 2019-11-03
584 Question Answering for Privacy Policies: Combining Computational and Legal Perspectives ABHILASHA RAVICHANDER et. al. We present PrivacyQA, a corpus consisting of 1750 questions about the privacy policies of mobile applications, and over 3500 expert annotations of relevant answers. CS.arxiv 2019-11-03
585 Constrained Co-embedding Model for User Profiling in Question Answering Communities YUPENG LUO et. al. In this paper, we study the problem of user profiling in question answering communities. CS.cikm 2019-11-03
586 Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms AVIKALP SRIVASTAVA et. al. With the increasingly multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. CS.cikm 2019-11-03
587 Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System YE LIU et. al. In order to eliminate the effect of ill-formed questions, we approach the question refinement task and propose a unified model, QREFINE, to refine the ill-formed questions to well-formed question. CS.cikm 2019-11-03
588 An Interactive Mechanism to Improve Question Answering Systems via Feedback XINBO ZHANG et. al. To provide better interactivity and online performance, we design a holistic graph mining algorithm (HWspan) to automatically refine the query graph. CS.cikm 2019-11-03
589 Attentive History Selection for Conversational Question Answering CHEN QU et. al. In this work, we propose a novel solution for ConvQA that involves three aspects. CS.cikm 2019-11-03
590 Learning to Answer Complex Questions over Knowledge Bases with Query Composition NIKITA BHUTANI et. al. We propose a KB-QA system, TextRay, which answers complex questions using a novel decompose-execute-join approach. CS.cikm 2019-11-03
591 Commonsense Properties from Query Logs and Question Answering Forums JULIEN ROMERO et. al. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. CS.cikm 2019-11-03
592 A Compare-Aggregate Model with Latent Clustering for Answer Selection SEUNGHYUN YOON et. al. In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing. CS.cikm 2019-11-03
593 Let’s Ask Again: Refine Network for Automatic Question Generation PREKSHA NEMA et. al. In this work, we focus on the task of Automatic Question Generation (AQG) where given a passage and an answer the task is to generate the corresponding question. CS.emnlp 2019-11-02
594 Answering Conversational Questions on Structured Data without Logical Forms THOMAS MUELLER et. al. We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation. CS.emnlp 2019-11-02
595 Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering SORAVIT CHANGPINYO et. al. In this paper, we examine the effect of decoupling box proposal and featurization for down-stream tasks. CS.emnlp 2019-11-02
596 Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations XIN LV et. al. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. CS.emnlp 2019-11-02
597 Effective Use of Transformer Networks for Entity Tracking ADITYA GUPTA et. al. In this paper, we explore the use of pre-trained transformer networks for entity tracking tasks in procedural text. CS.emnlp 2019-11-02
598 Exploring Diverse Expressions for Paraphrase Generation LIHUA QIAN et. al. In this paper, we propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. CS.emnlp 2019-11-02
599 Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling LINQING LIU et. al. However, such structure priors have not been well exploited in previous work for semantic modeling. To examine their effectiveness, we start with the Pairwise Word Interaction Model, one of the best models according to a recent reproducibility study, then introduce components for modeling context and structure using multi-layer BiLSTMs and TreeLSTMs. CS.emnlp 2019-11-02
600 A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning MINGHAO HU et. al. In this paper, we introduce the Multi-Type Multi-Span Network (MTMSN), a neural reading comprehension model that combines a multi-type answer predictor designed to support various answer types (e.g., span, count, negation, and arithmetic expression) with a multi-span extraction method for dynamically producing one or multiple text spans. CS.emnlp 2019-11-02
601 Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection NINA POERNER et. al. We address the problem of Duplicate Question Detection (DQD) in low-resource domain-specific Community Question Answering forums. CS.emnlp 2019-11-02
602 Fine-tune BERT with Sparse Self-Attention Mechanism BAIYUN CUI et. al. In this paper, we develop a novel Sparse Self-Attention Fine-tuning model (referred as SSAF) which integrates sparsity into self-attention mechanism to enhance the fine-tuning performance of BERT. CS.emnlp 2019-11-02
603 Social IQa: Commonsense Reasoning about Social Interactions MAARTEN SAP et. al. We introduce Social IQa, the first large-scale benchmark for commonsense reasoning about social situations. CS.emnlp 2019-11-02
604 Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base TAO SHEN et. al. To tackle these issues, we propose an innovative multi-task learning framework where a pointer-equipped semantic parsing model is designed to resolve coreference in conversations, and naturally empower joint learning with a novel type-aware entity detection model. CS.emnlp 2019-11-02
605 Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation ARIJIT RAY et. al. In this work, we introduce a dataset, ConVQA, and metrics that enable quantitative evaluation of consistency in VQA. Further, we propose a consistency-improving data augmentation module, a Consistency Teacher Module (CTM). CS.emnlp 2019-11-02
606 Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling JINFENG RAO et. al. To bridge this gap, we propose a novel model, HCAN{\textasciitilde}(Hybrid Co-Attention Network), that comprises (1) a hybrid encoder module that includes ConvNet-based and LSTM-based encoders, (2) a relevance matching module that measures soft term matches with importance weighting at multiple granularities, and (3) a semantic matching module with co-attention mechanisms that capture context-aware semantic relatedness. CS.emnlp 2019-11-02
607 Dual Attention Networks for Visual Reference Resolution in Visual Dialog GI-CHEON KANG et. al. In this paper, we propose Dual Attention Networks (DAN) for visual reference resolution in VisDial. CS.emnlp 2019-11-02
608 Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases CHRISTOPHER CLARK et. al. In this paper, we show that if we have prior knowledge of such biases, we can train a model to be more robust to domain shift. CS.emnlp 2019-11-02
609 Improving Answer Selection and Answer Triggering using Hard Negatives SAWAN KUMAR et. al. In this paper, we establish the effectiveness of using hard negatives, coupled with a siamese network and a suitable loss function, for the tasks of answer selection and answer triggering. CS.emnlp 2019-11-02
610 Can You Unpack That? Learning to Rewrite Questions-in-Context AHMED ELGOHARY et. al. We introduce the task of question-in-context rewriting: given the context of a conversation’s history, rewrite a context-dependent into a self-contained question with the same answer. CS.emnlp 2019-11-02
611 Single Training Dimension Selection for Word Embedding with PCA YU WANG In this paper, we present a fast and reliable method based on PCA to select the number of dimensions for word embeddings. CS.emnlp 2019-11-02
612 Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning LIFU HUANG et. al. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. CS.emnlp 2019-11-02
613 Learning with Limited Data for Multilingual Reading Comprehension KYUNGJAE LEE et. al. To address this challenge, we propose a weakly-supervised framework that quantifies such noises from automatically generated labels, to deemphasize or fix noisy data in training. CS.emnlp 2019-11-02
614 PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text HAITIAN SUN et. al. We describe PullNet, an integrated framework for (1) learning what to retrieve and (2) reasoning with this heterogeneous information to find the best answer. CS.emnlp 2019-11-02
615 Ranking and Sampling in Open-Domain Question Answering YANFU XU et. al. In this paper, we first introduce a ranking model leveraging the paragraph-question and the paragraph-paragraph relevance to compute a confidence score for each paragraph. Furthermore, based on the scores, we design a modified weighted sampling strategy for training to mitigate the influence of the noisy and distracting paragraphs. CS.emnlp 2019-11-02
616 What’s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering TUSHAR KHOT et. al. We propose jointly training a model to simultaneously fill this knowledge gap and compose it with the provided partial knowledge. CS.emnlp 2019-11-02
617 Video Dialog via Progressive Inference and Cross-Transformer WEIKE JIN et. al. In this paper, we introduce a novel progressive inference mechanism for video dialog, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous. CS.emnlp 2019-11-02
618 Movie Plot Analysis via Turning Point Identification PINELOPI PAPALAMPIDI et. al. We propose the task of turning point identification in movies as a means of analyzing their narrative structure. We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays. CS.emnlp 2019-11-02
619 MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering HOJAE HAN et. al. Specifically, we propose MICRON: Multigranular Interaction for Contextualizing RepresentatiON, a novel approach which derives contextualized uni-gram representation from n-grams. CS.emnlp 2019-11-02
620 PubMedQA: A Dataset for Biomedical Research Question Answering QIAO JIN et. al. We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. CS.emnlp 2019-11-02
621 Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering JIWEI DING et. al. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. CS.emnlp 2019-11-02
622 Revisiting the Evaluation of Theory of Mind through Question Answering MATTHEW LE et. al. In this work, we revisit the evaluation of theory of mind through question answering. CS.emnlp 2019-11-02
623 Do NLP Models Know Numbers? Probing Numeracy in Embeddings ERIC WALLACE et. al. We begin by investigating the numerical reasoning capabilities of a state-of-the-art question answering model on the DROP dataset. We find this model excels on questions that require numerical reasoning, i.e., it already captures numeracy. CS.emnlp 2019-11-02
624 Answers Unite! Unsupervised Metrics for Reinforced Summarization Models THOMAS SCIALOM et. al. We thus explore and propose alternative evaluation measures: the reported human-evaluation analysis shows that the proposed metrics, based on Question Answering, favorably compare to ROUGE — with the additional property of not requiring reference summaries. CS.emnlp 2019-11-02
625 Revealing the Importance of Semantic Retrieval for Machine Reading at Scale YIXIN NIE et. al. In this work, we give general guidelines on system design for MRS by proposing a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task. CS.emnlp 2019-11-02
626 GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level ZIXIAN HUANG et. al. In this paper, we introduce the GeoSQA dataset. CS.emnlp 2019-11-02
627 Generating Highly Relevant Questions JIAZUO QIU et. al. The neural seq2seq based question generation (QG) is prone to generating generic and undiversified questions that are poorly relevant to the given passage and target answer. In this paper, we propose two methods to address the issue. CS.emnlp 2019-11-02
628 A Discrete Hard EM Approach for Weakly Supervised Question Answering SEWON MIN et. al. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible solutions (e.g. different mentions or equations) that contains one correct option. CS.emnlp 2019-11-02
629 Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering ZHIGUO WANG et. al. To tackle this issue, we propose a multi-passage BERT model to globally normalize answer scores across all passages of the same question, and this change enables our QA model find better answers by utilizing more passages. CS.emnlp 2019-11-02
630 Incorporating External Knowledge into Machine Reading for Generative Question Answering BIN BI et. al. In this paper, we propose a new neural model, Knowledge-Enriched Answer Generator (KEAG), which is able to compose a natural answer by exploiting and aggregating evidence from all four information sources available: question, passage, vocabulary and knowledge. CS.emnlp 2019-11-02
631 How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering SANJAY KAMATH et. al. We compare two pre-training models based on reading comprehension and open domain question answering models and determine the performance when fine-tuned and tested over BIOASQ question answering dataset. CS.arxiv 2019-11-02
632 Answering Complex Open-domain Questions Through Iterative Query Generation PENG QI et. al. We present GoldEn (Gold Entity) Retriever, which iterates between reading context and retrieving more supporting documents to answer open-domain multi-hop questions. CS.emnlp 2019-11-02
633 Finding Generalizable Evidence by Learning to Convince Qtextbackslash&A Models ETHAN PEREZ et. al. We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. CS.emnlp 2019-11-02
634 Interactive Language Learning by Question Answering XINGDI YUAN et. al. We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents. CS.emnlp 2019-11-02
635 Language Models as Knowledge Bases? FABIO PETRONI et. al. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. CS.emnlp 2019-11-02
636 QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization YI-TING YEH et. al. To address this problem, we propose QAInfomax as a regularizer in reading comprehension systems by maximizing mutual information among passages, a question, and its answer. CS.emnlp 2019-11-02
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