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Recent studies report that many machine reading comprehension (MRC) models can perform closely to or even better than humans on benchmark datasets.
Sogou machine reading comprehension toolkit
Jindou Wu, Yunlun Yang, Chao Deng, Hongyi Tang, Bingning Wang, Haoze Sun, Ting Yao, and Qi Zhang. 2019 · 1903
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Did the model understand the question?
Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere. 2018 · 1906
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What does bert learn from multiple-choice reading comprehension datasets?
Chenglei Si, Shuohang Wang, Min-Yen Kan, and Jing Jiang. 2019 · 1910
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The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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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 · 2014
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A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D Manning. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Hybrid question answering over knowledge base and free text
Kun Xu, Yansong Feng, Songfang Huang, and Dongyan Zhao. 2016 · 2016
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Learning to paraphrase for question answering
Li Dong, Jonathan Mallinson, Siva Reddy, and Mirella Lapata. 2017 · 2017
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Bidirectional attention flow for machine comprehension
Seo Minjoon, Kembhavi Aniruddha, Farhadi Ali, and Hajishirzi Hannaneh. 2017 · 2017
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Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Cited alongside, same era.
Making neural QA as simple as possible but not simpler
Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017 · 2017
Cited alongside, same era.
How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C Lipton. 2018 · 2018
Cited alongside, same era.
What makes reading comprehension questions easier?
Saku Sugawara, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Quoref: A reading comprehension dataset with questions requiring coreferential reasoning
Lattice cnns for matching based chinese question answering
Yuxuan Lai, Yansong Feng, Xiaohan Yu, Zheng Wang, Kun Xu, and Dongyan Zhao. 2019 · 2019
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Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019 · 2019
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Has-qa: Hierarchical answer spans model for open-domain question answering
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Lixin Su, and Xueqi Cheng. 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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Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 2019
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Beat the ai: Investigating adversarial human annotation for reading comprehension
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Pradeep Dasigi, Nelson F. Liu, Ana Marasović, Noah A. Smith, and Matt Gardner. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
Cited alongside, same era.
Avoiding reasoning shortcuts: Adversarial evaluation, training, and model development for multi-hop QA
Yichen Jiang and Mohit Bansal. 2019 · 2019
Cited alongside, same era.
SGD on neural networks learns functions of increasing complexity
Dimitris Kalimeris, Gal Kaplun, Preetum Nakkiran, Benjamin L Edelman, Tristan Yang, Boaz Barak, and Haofeng Zhang. 2019 · 2019
Cited alongside, same era.
Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Cited alongside, same era.
Max Bartolo, A. Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp. 2020 · 2020
Later among the works it cites.
Span selection pre-training for question answering
Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, and Avi Sil. 2020 · 2020
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Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2020
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Learning to explain: Datasets and models for identifying valid reasoning chains in multihop question-answering
Harsh Jhamtani and Peter Clark. 2020 · 2020
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Assessing the benchmarking capacity of machine reading comprehension datasets
Saku Sugawara, Pontus Stenetorp, Kentaro Inui, and Akiko Aizawa. 2020 · 2020
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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