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Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets.
Did the Model Understand the Question?
Pramod K. Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere. 2018 · 1906
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Open information extraction from the web
Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S. Weld. 2008 · 2008
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Leveraging linguistic structure for open domain information extraction
Gabor Angeli, Melvin Johnson Premkumar, and Christopher D. Manning. 2015 · 2015
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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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e-SNLI : Natural Language Inference with Natural Language Explanations
Oana-maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?
Peter A. Jansen. 2018 · 2018
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Peter A. Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton T. Morrison. 2018 · 2018
Cited alongside, same era.
The NarrativeQA Reading Comprehension Challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
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What Makes Reading Comprehension Questions Easier?
Saku Sugawara, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. 2018 · 2018
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Automated Fact Checking: Task formulations, methods and future directions
James Thorne and Andreas Vlachos. 2018 · 2018
Cited alongside, same era.
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Understanding Dataset Design Choices for Multi-hop Reasoning
Jifan Chen and Greg Durrett. 2019 · 2019
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ELI5: Long Form Question Answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019 · 2019
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Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension
Yichen Jiang, Nitish Joshi, Yen-Chun Chen, and Mohit Bansal. 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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Explain Yourself ! Leveraging Language Models for Commonsense Reasoning
Fatema Nanzneen Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Cited alongside, same era.
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Cited alongside, same era.
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