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Reading comprehension is one of the crucial tasks for furthering research in natural language understanding.
Multiqa: An empirical investigation of generalization and transfer in reading comprehension
Alon Talmor and Jonathan Berant. 2019 · 1905
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Reasoning over paragraph effects in situations
Kevin Lin, Oyvind Tafjord, Peter Clark, and Matt Gardner. 2019 · 1908
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SQuAD: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang. 2016 · 2016
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Towards AI-complete question answering: A set of prerequisite toy tasks
J. Weston, A. Bordes, S. Chopra, and T. Mikolov. 2016 · 2016
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Adversarial examples for evaluating reading comprehension systems
R. Jia and P. Liang. 2017 · 2017
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Parlai: A dialog research software platform
A. H. Miller, W. Feng, A. Fisch, J. Lu, D. Batra, A. Bordes, D. Parikh, and J. Weston. 2017 · 2017
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NewsQA: A machine comprehension dataset
A. Trischler, T. Wang, X. Yuan, J. Harris, A. Sordoni, P. Bachman, and K. Suleman. 2017 · 2017
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Senteval: An evaluation toolkit for universal sentence representations
A. Conneau and D. Kiela. 2018 · 2018
Cited alongside, same era.
The narrativeqa reading comprehension challenge
T. Kočiský, J. Schwarz, P. Blunsom, C. Dyer, K. Hermann, G. Melis, and E. Grefenstette. 2018 · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for SQuAD
P. Rajpurkar, R. Jia, and P. Liang. 2018 · 2018
Cited alongside, same era.
Semantically equivalent adversarial rules for debugging nlp models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
Cited alongside, same era.
Duorc: Towards complex language understanding with paraphrased reading comprehension
A. Saha, R. Aralikatte, M. Khapra, and K. Sankaranarayanan. 2018 · 2018
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Quoref: A reading comprehension dataset with questions requiring coreferential reasoning
Pradeep Dasigi, Nelson Liu, Ana Marasovic, Noah Smith, and Matt Gardner. 2019 · 2019
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, and M. Gardner. 2019 · 2019
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Are red roses red? evaluating consistency of question-answering models
Marco Tulio Ribeiro, Carlos Guestrin, and Sameer Singh. 2019 · 2019
Closest in time.
BERT and PALs: Projected attention layers for efficient adaptation in multi-task learning
Asa Cooper Stickland and Iain Murray. 2019 · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman. 2019 · 2019
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova. 2019a
Cited in the paper.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019b
Cited in the paper.
Haichao Zhu, Li Dong, Furu Wei, Wenhui Wang, Bing Qin, and Ting Liu. 2019 · 2019
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