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Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document.
ERNIE: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019b · 1904
Earlier work this paper cites.
Pre-training with whole word masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, and Guoping Hu. 2019 · 1906
Earlier work this paper cites.
Background knowledge and reading comprehension
Marilyn Adams and Bertram Bruce. 1982 · 1982
Earlier work this paper cites.
Isolating domain dependencies in natural language interfaces
Ralph Grishman, Lynette Hirschman, and Carol Friedman. 1983 · 1983
Earlier work this paper cites.
CYC: Using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks
Douglas B Lenat, Mayank Prakash, and Mary Shepherd. 1985 · 1985
Earlier work this paper cites.
A taxonomy of part-whole relations
Morton E Winston, Roger Chaffin, and Douglas Herrmann. 1987 · 1987
Earlier work this paper cites.
Reasoning with a domain model
Steffen Leo Hansen. 1994 · 1994
Earlier work this paper cites.
WordNet: An electronic lexical database
George Miller. 1998 · 1998
Earlier work this paper cites.
The part-of-speech tagging guidelines for the Penn Chinese Treebank (3.0)
Fei Xia. 2000 · 2000
Earlier work this paper cites.
Near-synonymy and lexical choice
Philip Edmonds and Graeme Hirst. 2002 · 2002
Earlier work this paper cites.
Can we derive general world knowledge from texts?
Lenhart Schubert. 2002 · 2002
Earlier work this paper cites.
The relationship between depth of vocabulary knowledge and l2 learners’ lexical inferencing strategy use and success
Hossein Nassaji. 2006 · 2006
Earlier work this paper cites.
Machine reading at the University of Washington
Hoifung Poon, Janara Christensen, Pedro Domingos, Oren Etzioni, Raphael Hoffmann, Chloe Kiddon, Thomas Lin, Xiao Ling, Mausam, Alan Ritter, Stefan Schoenmackers, Stephen Soderland, Dan Weld, Fei Wu, and Congle Zhang. 2010 · 2010
Earlier work this paper cites.
Types of common-sense knowledge needed for recognizing textual entailment
Peter LoBue and Alexander Yates. 2011 · 2011
Earlier work this paper cites.
Connotation lexicon: A dash of sentiment beneath the surface meaning
Song Feng, Jun Seok Kang, Polina Kuznetsova, and Yejin Choi. 2013 · 2013
Earlier work this paper cites.
MCTest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013 · 2013
Earlier work this paper cites.
Open question answering over curated and extracted knowledge bases
Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. 2014 · 2014
Earlier work this paper cites.
Challenges of adding causation to richer event descriptions
Rei Ikuta, Will Styler, Mariah Hamang, Tim O’Gorman, and Martha Palmer. 2014 · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Embracing data abundance: Booktest dataset for reading comprehension
Ondrej Bajgar, Rudolf Kadlec, and Jan Kleindienst. 2016 · 2016
Cited alongside, same era.
Taking up the gaokao challenge: An information retrieval approach
Gong Cheng, Weixi Zhu, Ziwei Wang, Jianghui Chen, and Yuzhong Qu. 2016 · 2016
Cited alongside, same era.
Combining retrieval, statistics, and inference to answer elementary science questions
Peter Clark, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter D Turney, and Daniel Khashabi. 2016 · 2016
Cited alongside, same era.
Consensus attention-based neural networks for Chinese reading comprehension
Yiming Cui, Ting Liu, Zhipeng Chen, Shijin Wang, and Guoping Hu. 2016 · 2016
Cited alongside, same era.
The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016 · 2016
Cited alongside, same era.
The NarrativeQA reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gáabor Melis, and Edward Grefenstette. 2018 · 2018
Later among the works it cites.
Analogical reasoning on chinese morphological and semantic relations
Shen Li, Zhe Zhao, Renfen Hu, Wensi Li, Tao Liu, and Xiaoyong Du. 2018 · 2018
Later among the works it cites.
SemEval-2018 Task 11: Machine comprehension using commonsense knowledge
Simon Ostermann, Michael Roth, Ashutosh Modi, Stefan Thater, and Manfred Pinkal. 2018 · 2018
Later among the works it cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Later among the works it cites.
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Dataset and neural recurrent sequence labeling model for open-domain factoid question answering
Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu. 2016 · 2016
Cited alongside, same era.
A corpus and evaluation framework for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
Cited alongside, same era.
MS MARCO: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Cited alongside, same era.
Richer event description: Integrating event coreference with temporal, causal and bridging annotation
Tim O’Gorman, Kristin Wright-Bettner, and Martha Palmer. 2016 · 2016
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
IJCNLP-2017 Task 5: Multi-choice question answering in examinations
Shangmin Guo, Kang Liu, Shizhu He, Cao Liu, Jun Zhao, and Zhuoyu Wei. 2017a · 2017
Cited alongside, same era.
DuReader: a Chinese machine reading comprehension dataset from real-world applications
Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, Yizhong Wang, Hua Wu, Qiaoqiao She, Xuan Liu, Tian Wu, and Haifeng Wang. 2017 · 2017
Cited alongside, same era.
Chih Chieh Shao, Trois Liu, Yuting Lai, Yiying Tseng, and Sam Tsai. 2018 · 2018
Later among the works it cites.
We usually don’t like going to the dentist: Using common sense to detect irony on twitter
Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018 · 2018
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A co-matching model for multi-choice reading comprehension
Shuohang Wang, Mo Yu, Shiyu Chang, and Jing Jiang. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Later among the works it cites.
Large-scale cloze test dataset created by teachers
Qizhe Xie, Guokun Lai, Zihang Dai, and Eduard Hovy. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
Later among the works it cites.
One-shot learning for question-answering in Gaokao history challenge
Zhuosheng Zhang and Hai Zhao. 2018 · 2018
Later among the works it cites.
BoolQ: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Exploiting sentence embedding for medical question answering
Yu Hao, Xien Liu, Ji Wu, and Ping Lv. 2019 · 2019
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GeoSQA: A benchmark for scenario-based question answering in the geography domain at high school level
Zixian Huang, Yulin Shen, Xiao Li, Yuang Wei, Gong Cheng, Lin Zhou, Xinyu Dai, and Yuzhong Qu. 2019 · 2019
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CoQA: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning. 2019 · 2019
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ChID: A large-scale Chinese idiom dataset for cloze test
Chujie Zheng, Minlie Huang, and Aixin Sun. 2019 · 2019
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