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Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure.
Option comparison network for multiple-choice reading comprehension
Qiu Ran, Peng Li, Weiwei Hu, and Jie Zhou. 2019 · 1903
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Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015 · 2015
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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
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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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RACE: Large-scale ReAding Comprehension Dataset From Examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
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Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
Improving Machine Reading Comprehension with General Reading Strategies
Kai Sun, Dian Yu, Dong Yu, and Claire Cardie. 2018 · 2018
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Multi-range Reasoning for Machine Comprehension
Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018 · 2018
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A Co-Matching Model for Multi-choice Reading Comprehension
Shuohang Wang, Mo Yu, Jing Jiang, and Shiyu Chang. 2018 · 2018
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Hierarchical Attention Flow for Multiple-choice Reading Comprehension
Haichao Zhu, Furu Wei, Bing Qin, and Ting Liu. 2018 · 2018
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Multi-Matching Network for Multiple Choice Reading Comprehension
Min Tang, Jiaran Cai, and Hankz Hankui Zhuo. 2019 · 2019
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Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
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Improving Language Understanding by Generative Pre-Training
Alec Radford. 2018 · 2018
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