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The task of Question Answering (QA) has attracted significant research interest for long.
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“Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning”
Ronald. Williams · 1992
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“WordNet: A Lexical Database for English”
George. Miller · 1995
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“Long short-term memory”
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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“Long short-term memory”
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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“Information fusion in the context of multi-document summarization”
Regina Barzilay, Kathleen McKeown and Michael Elhadad · 1999
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“Multi-document summarization by sentence extraction”
Jade Goldstein, Vibhu Mittal, Jaime Carbonell and Mark Kantrowitz · 2000
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“Cumulated Gain-Based Evaluation of IR Techniques”
Kalervo Järvelin and Jaana Kekäläinen · 2002
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“Bleu: a method for automatic evaluation of machine translation”
Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu · 2002
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“Logical Natural Language Generation from Open-Domain Tables”
Wenhu Chen et al · 2004
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“ROUGE: A Package for Automatic Evaluation of Summaries”
Chin-Yew Lin · 2004
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“METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments”
Satanjeev Banerjee and Alon Lavie · 2005
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“One-Shot Learning of Object Categories”
Li Fei-Fei, Rob Fergus and Pietro Perona · 2006
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“DeBERTa: Decoding-enhanced BERT with Disentangled Attention”, 2021
Pengcheng He, Xiaodong Liu, Jianfeng Gao and Weizhu Chen · 2006
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“DrugBank: a knowledgebase for drugs, drug actions and drug targets”
David Wishart et al · 2008
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“Exploring content models for multi-document summarization”
Aria Haghighi and Lucy Vanderwende · 2009
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“Mean average precision”
Ling Liu and M Özsu · 2009
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“Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval”
Wenhan Xiong et al · 2009
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“Evolutionary timeline summarization: a balanced optimization framework via iterative substitution”
Rui Yan et al · 2011
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“The question answering systems: A survey”
Ali Allam and Mohamed Haggag · 2012
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“Abstract Meaning Representation for Sembanking”
Laura Banarescu et al · 2013
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“Methods for exploring and mining tables on wikipedia”
Chandra Bhagavatula, Thanapon Noraset and Doug Downey · 2013
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“Estimating document focus time”
Adam Jatowt, Ching-man Yeung and Katsumi Tanaka · 2013
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“Generating Natural Language Questions to Support Learning On-Line”
David Lindberg, Fred Popowich, John Nesbit and Phil Winne · 2013
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“Area under the ROC Curve”
Francisco Melo · 2013
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“Learning phrase representations using RNN encoder-decoder for statistical machine translation”
Kyunghyun Cho et al · 2014
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Alex Graves, Greg Wayne and Ivo Danihelka · 2014
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“Glove: Global vectors for word representation”
Jeffrey Pennington, Richard Socher and Christopher Manning · 2014
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“Cider: consensus-based image description evaluation. CoRR”
Ramakrishna Vedantam, C Zitnick and Devi Parikh · 2014
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“Question answering systems: survey and trends”
Abdelghani Bouziane, Djelloul Bouchiha, Noureddine Doumi and Mimoun Malki · 2015
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“A large annotated corpus for learning natural language inference”
Samuel Bowman, Gabor Angeli, Christopher Potts and Christopher Manning · 2015
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“Teaching machines to read and comprehend”
Karl Hermann et al · 2015
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“From word embeddings to document distances”
Matt Kusner, Yu Sun, Nicholas Kolkin and Kilian Weinberger · 2015
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“A critical review of recurrent neural networks for sequence learning”
Zachary Lipton, John Berkowitz and Charles Elkan · 2015
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“Effective Approaches to Attention-based Neural Machine Translation”
Thang Luong, Hieu Pham and Christopher. Manning · 2015
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Leila Arras et al · 2016
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“Incorporating Copying Mechanism in Sequence-to-Sequence Learning”
Jiatao Gu, Zhengdong Lu, Hang Li and Victor.K. Li · 2016
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“Pointing the Unknown Words”
Caglar Gulcehre et al · 2016
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“Semi-Supervised Classification with Graph Convolutional Networks”
Thomas. Kipf and Max Welling · 2016
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“Semi-Supervised Classification with Graph Convolutional Networks”
Thomas. Kipf and Max Welling · 2016
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“A survey on question answering systems with classification”
Amit Mishra and Sanjay Jain · 2016
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“Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond”
Ramesh Nallapati et al · 2016
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“SQuAD: 100,000+ Questions for Machine Comprehension of Text”
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev and Percy Liang · 2016
Earlier work this paper cites.
“SQuAD: 100,000+ Questions for Machine Comprehension of Text”
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev and Percy Liang · 2016
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“Easy Questions First? A Case Study on Curriculum Learning for Question Answering”
Mrinmaya Sachan and Eric Xing · 2016
Earlier work this paper cites.
“Bidirectional Attention Flow for Machine Comprehension”
Min Seo, Aniruddha Kembhavi, Ali Farhadi and Hannaneh Hajishirzi · 2016
Earlier work this paper cites.
“ConceptNet 5.5: An Open Multilingual Graph of General Knowledge. Singh 2002 (2016)”
Robyn Speer, Joshua Chin and Catherine Havasi · 2016
Earlier work this paper cites.
“NewsQA: A Machine Comprehension Dataset”
Adam Trischler et al · 2016
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“Machine comprehension using match-lstm and answer pointer”
Shuohang Wang and Jing Jiang · 2016
Earlier work this paper cites.
“Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”
Yonghui Wu et al · 2016
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“A causal framework for explaining the predictions of black-box sequence-to-sequence models”
David Alvarez-Melis and Tommi Jaakkola · 2017
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“Explanation and justification in machine learning: A survey”
Or Biran and Courtenay Cotton · 2017
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“Reading Wikipedia to Answer Open-Domain Questions”
Danqi Chen, Adam Fisch, Jason Weston and Antoine Bordes · 2017
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“Enhanced LSTM for Natural Language Inference”
Qian Chen et al · 2017
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“Recurrent Hidden Semi-Markov Model”
Hanjun Dai et al · 2017
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“Learning to Ask: Neural Question Generation for Reading Comprehension”
Xinya Du, Junru Shao and Claire Cardie · 2017
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“Annotation Artifacts in Natural Language Inference Data”
Suchin Gururangan et al · 2017
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“Survey on challenges of question answering in the semantic web”
Konrad Höffner et al · 2017
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“Knowledge Base Completion: Baselines Strike Back”
Rudolf Kadlec, Ondrej Bajgar and Jan Kleindienst · 2017
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“Adversarial Learning for Neural Dialogue Generation”
Jiwei Li et al · 2017
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“Why We Need New Evaluation Metrics for NLG”
Jekaterina Novikova, Ondřej Dušek, Amanda Cercas and Verena Rieser · 2017
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“Self-Critical Sequence Training for Image Captioning”
Steven. Rennie et al · 2017
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Wojciech Samek, Thomas Wiegand and Klaus-Robert Müller · 2017
Earlier work this paper cites.
“Modeling Relational Data with Graph Convolutional Networks (2017)”
Michael Schlichtkrull et al · 2017
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“Get To The Point: Summarization with Pointer-Generator Networks”
Abigail See, Peter Liu and Christopher Manning · 2017
Earlier work this paper cites.
“Get to the point: Summarization with pointer-generator networks”
Abigail See, Peter Liu and Christopher Manning · 2017
Earlier work this paper cites.
“Question Answering and Question Generation as Dual Tasks”
Duyu Tang, Nan Duan, Tao Qin and Ming Zhou · 2017
Earlier work this paper cites.
“Attention is all you need”
Ashish Vaswani et al · 2017
Earlier work this paper cites.
“Attention is all you need”
Ashish Vaswani et al · 2017
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“Visual question answering: A survey of methods and datasets”
Qi Wu et al · 2017
Earlier work this paper cites.
“Sentence Simplification with Deep Reinforcement Learning”
Xingxing Zhang and Mirella Lapata · 2017
Earlier work this paper cites.
“Commonsense for Generative Multi-Hop Question Answering Tasks”
Lisa Bauer, Yicheng Wang and Mohit Bansal · 2018
Earlier work this paper cites.
“QuAC : Question Answering in Context”
Eunsol Choi et al · 2018
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“Think you have solved question answering? try arc, the ai2 reasoning challenge”
Peter Clark et al · 2018
Earlier work this paper cites.
“Transforming Question Answering Datasets Into Natural Language Inference Datasets”
Dorottya Demszky, Kelvin Guu and Percy Liang · 2018
Earlier work this paper cites.
“Core techniques of question answering systems over knowledge bases: a survey”
Dennis Diefenbach, Vanessa Lopez, Kamal Singh and Pierre Maret · 2018
Earlier work this paper cites.
“Difficulty Controllable Question Generation for Reading Comprehension”
Yifan Gao et al · 2018
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“Survey of the state of the art in natural language generation: Core tasks, applications and evaluation”
Albert Gatt and Emiel Krahmer · 2018
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“Explaining explanations: An overview of interpretability of machine learning”
Leilani Gilpin et al · 2018
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“Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?”
Peter Jansen · 2018
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“WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference”
Peter Jansen, Elizabeth Wainwright, Steven Marmorstein and Clayton Morrison · 2018
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“Looking beyond the surface: A challenge set for reading comprehension over multiple sentences”
Daniel Khashabi et al · 2018
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“Bilinear attention networks”
Jin-Hwa Kim, Jaehyun Jun and Byoung-Tak Zhang · 2018
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“The narrativeqa reading comprehension challenge”
Tomáš Kočiskỳ et al · 2018
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“Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering”
“Answering Event-Related Questions over Long-Term News Article Archives”
Jiexin Wang, Adam Jatowt, Michael Färber and Masatoshi Yoshikawa · 2020
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“A comprehensive survey on graph neural networks”
Zonghan Wu et al · 2020
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“Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering”
Vikas Yadav, Steven Bethard and Mihai Surdeanu · 2020
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“Low-Resource Generation of Multi-hop Reasoning Questions”
Jianxing Yu et al · 2020
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“Coarse and Fine Granularity Graph Reasoning for Interpretable Multi-Hop Question Answering”
Min Zhang et al · 2020
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“Event Detection as Question Answering with Entity Information”
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Todor Mihaylov, Peter Clark, Tushar Khot and Ashish Sabharwal · 2018
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“Efficient and Robust Question Answering from Minimal Context over Documents”
Sewon Min, Victor Zhong, Richard Socher and Caiming Xiong · 2018
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“Abstractive unsupervised multi-document summarization using paraphrastic sentence fusion”
Mir Nayeem, Tanvir Fuad and Yllias Chali · 2018
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“Towards a Better Metric for Evaluating Question Generation Systems”
Preksha Nema and Mitesh. Khapra · 2018
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“Deep Contextualized Word Representations”
Matthew. Peters et al · 2018
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“Interpretation of Natural Language Rules in Conversational Machine Reading”
Marzieh Saeidi et al · 2018
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“Introducing mathqa: a math-aware question answering system”
Moritz Schubotz et al · 2018
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Emanuela Boros, Jose. Moreno and Antoine Doucet · 2021
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“Coarse-grained decomposition and fine-grained interaction for multi-hop question answering”
Xing Cao and Yun Liu · 2021
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“FinQA: A Dataset of Numerical Reasoning over Financial Data”
Zhiyu Chen et al · 2021
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“Training Verifiers to Solve Math Word Problems”, 2021
Karl Cobbe et al · 2021
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“A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers”
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“Generative Context Pair Selection for Multi-hop Question Answering”
Dheeru Dua et al · 2021
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“Measuring mathematical problem solving with the math dataset”
Dan Hendrycks et al · 2021
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“Breadth First Reasoning Graph for Multi-hop Question Answering”
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“A survey on complex knowledge base question answering: Methods, challenges and solutions”, 2021
Yunshi Lan et al · 2021
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“Medical Visual Question Answering: A Survey”
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“Unsupervised Multi-hop Question Answering by Question Generation”
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“Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections”, Synthesis Lectures on Information Concepts, Retrieval, and Services
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“Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering”
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“Iterative Hierarchical Attention for Answering Complex Questions over Long Documents”
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“Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?”
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“Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX”, https://github.com/kingoflolz/mesh-transformer-jax , 2021
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“Exploiting Reasoning Chains for Multi-hop Science Question Answering”, 2021
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