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Recent pretrained language models "solved" many reading comprehension benchmarks, where questions are written with access to the evidence document.
A bert baseline for the natural questions
Chris Alberti, Kenton Lee, and Michael Collins. 2019 · 1901
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Unified open-domain question answering with structured and unstructured knowledge
Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Scott Yih. 2020 · 2012
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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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QuAC: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018 · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
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How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C. Lipton. 2018 · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 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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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
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Understanding dataset design choices for multi-hop reasoning
Jifan Chen and Greg Durrett. 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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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
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Read+ verify: Machine reading comprehension with unanswerable questions
Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, and Dongsheng Li. 2019 · 2019
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Natural Questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Question answering for privacy policies: Combining computational and legal perspectives
Abhilasha Ravichander, Alan W Black, Shomir Wilson, Thomas Norton, and Norman Sadeh. 2019 · 2019
Selective question answering under domain shift
Amita Kamath, Robin Jia, and Percy Liang. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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RikiNet: Reading wikipedia pages for natural question answering
Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, and Nan Duan. 2020 · 2020
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Transformers: State-of-the-art natural language processing
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CoQA: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019 · 2019
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XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Cited alongside, same era.
A qualitative comparison of CoQA, SQuAD 2.0 and QuAC
Mark Yatskar. 2019 · 2019
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ETC: Encoding long and structured data in transformers
Joshua Ainslie, Santiago Ontañón, Chris Alberti, Philip Pham, Anirudh Reddy Ravula, and Sumit Sanghai. 2020 · 2020
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TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages
Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020 · 2020
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Manymodalqa: Modality disambiguation and qa over diverse inputs
Darryl Hannan, Akshay Jain, and Mohit Bansal. 2020 · 2020
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MOCHA: A dataset for training and evaluating generative reading comprehension metrics
Anthony Chen, Gabriel Stanovsky, S. Singh, and Matt Gardner. 2020a
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SG-Net: Syntax-guided machine reading comprehension
Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, and Hai Zhao. 2020 · 2020
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XOR QA: Cross-lingual open-retrieval question answering
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Open question answering over tables and text
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