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Machine reading comprehension (MRC) is an AI challenge that requires machine to determine the correct answers to questions based on a given passage.
RoBERTa: A robustly optimized BERT pretraining approach
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Note on the sampling error of the difference between correlated proportions or percentages
McNemar, Q. 1947 · 1947
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Literacy as Multidimensional: Locating Information and Reading Comprehension
Guthrie, J. T.; and Mosenthal, P. 1987 · 1987
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Dual multi-head co-attention for multi-choice reading comprehension
Zhu, P.; Zhao, H.; and Li, X. 2020 · 2001
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Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
Zhang, Z.; Zhao, H.; and Wang, R. 2020 · 2005
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Teaching machines to read and comprehend
Hermann, K. M.; Kocisky, T.; Grefenstette, E.; Espeholt, L.; Kay, W.; Suleyman, M.; and Blunsom, P. 2015 · 2015
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The Goldilocks Principle: Reading Children’s Books with Explicit Memory Representations
Hill, F.; Bordes, A.; Chopra, S.; and Weston, J. 2015 · 2015
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A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
Chen, D.; Bolton, J.; and Manning, C. D. 2016 · 2016
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Text Understanding with the Attention Sum Reader Network
Kadlec, R.; Schmid, M.; Bajgar, O.; and Kleindienst, J. 2016 · 2016
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016 · 2016
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Bidirectional Attention Flow for Machine Comprehension
Seo, M.; Kembhavi, A.; Farhadi, A.; and Hajishirzi, H. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Y.; Schuster, M.; Chen, Z.; Le, Q. V.; Norouzi, M.; Macherey, W.; Krikun, M.; Cao, Y.; Gao, Q.; Macherey, K.; et al. 2016 · 2016
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Attention-over-Attention Neural Networks for Reading Comprehension
Cui, Y.; Chen, Z.; Wei, S.; Wang, S.; Liu, T.; and Hu, G. 2017 · 2017
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Gated-Attention Readers for Text Comprehension
Dhingra, B.; Liu, H.; Yang, Z.; Cohen, W. W.; and Salakhutdinov, R. 2017 · 2017
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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M.; Choi, E.; Weld, D.; and Zettlemoyer, L. 2017 · 2017
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RACE: Large-scale ReAding Comprehension Dataset From Examinations
Lai, G.; Xie, Q.; Liu, H.; Yang, Y.; and Hovy, E. 2017 · 2017
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NewsQA: A Machine Comprehension Dataset
Trischler, A.; Wang, T.; Yuan, X.; Harris, J.; Sordoni, A.; Bachman, P.; and Suleman, K. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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Machine Comprehension Using Match-LSTM and Answer Pointer
Wang, S.; and Jiang, J. 2017 · 2017
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Gated Self-Matching Networks for Reading Comprehension and Question Answering
Wang, W.; Yang, N.; Wei, F.; Chang, B.; and Zhou, M. 2017 · 2017
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Reading Twice for Natural Language Understanding
Weissenborn, D. 2017 · 2017
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Neural Structural Correspondence Learning for Domain Adaptation
Ziser, Y.; and Reichart, R. 2017 · 2017
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QuAC: Question Answering in Context
Choi, E.; He, H.; Iyyer, M.; Yatskar, M.; Yih, W.-t.; Choi, Y.; Liang, P.; and Zettlemoyer, L. 2018 · 2018
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Read+ verify: Machine reading comprehension with unanswerable questions
Hu, M.; Wei, F.; Peng, Y.; Huang, Z.; Yang, N.; and Li, D. 2019 · 2019
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Dependency or span, end-to-end uniform semantic role labeling
Li, Z.; He, S.; Zhao, H.; Zhang, Y.; Zhang, Z.; Zhou, X.; and Zhou, X. 2019 · 2019
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ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Lu, J.; Batra, D.; Parikh, D.; and Lee, S. 2019 · 2019
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CoQA: A Conversational Question Answering Challenge
Reddy, S.; Chen, D.; and Manning, C. D. 2019 · 2019
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XLNET: Generalized autoregressive pretraining for language understanding
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A Question-Focused Multi-Factor Attention Network for Question Answering
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A unified syntax-aware framework for semantic role labeling
Li, Z.; He, S.; Cai, J.; Zhang, Z.; Zhao, H.; Liu, G.; Li, L.; and Si, L. 2018 · 2018
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Stochastic Answer Networks for SQuAD 2.0
Liu, X.; Li, W.; Fang, Y.; Kim, A.; Duh, K.; and Gao, J. 2018 · 2018
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Deep contextualized word representations
Peters, M. E.; Neumann, M.; Iyyer, M.; Gardner, M.; Clark, C.; Lee, K.; and Zettlemoyer, L. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; and Sutskever, I. 2018 · 2018
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Know What You Don’t Know: Unanswerable Questions for SQuAD
Rajpurkar, P.; Jia, R.; and Liang, P. 2018 · 2018
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Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R. R.; and Le, Q. V. 2019 · 2019
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Explicit Contextual Semantics for Text Comprehension
Zhang, Z.; Wu, Y.; Li, Z.; and Zhao, H. 2019 · 2019
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Human Behavior Inspired Machine Reading Comprehension
Zheng, Y.; Mao, J.; Liu, Y.; Ye, Z.; Zhang, M.; and Ma, S. 2019 · 2019
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NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension
Back, S.; Chinthakindi, S. C.; Kedia, A.; Lee, H.; and Choo, J. 2020 · 2020
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Explicit Sentence Compression for Neural Machine Translation
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Answer Span Correction in Machine Reading Comprehension
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Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue
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Topic-aware multi-turn dialogue modeling
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