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BERT model has been successfully applied to open-domain QA tasks.
A bert baseline for the natural questions
Chris Alberti, Kenton Lee, and Michael Collins. 2019 · 1901
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Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019 · 1902
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Multi-perspective context matching for machine comprehension
Zhiguo Wang, Haitao Mi, Wael Hamza, and Radu Florian. 2016 · 2016
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Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Quasar: Datasets for question answering by search and reading
Bhuwan Dhingra, Kathryn Mazaitis, and William W Cohen. 2017 · 2017
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Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017 · 2017
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Christopher Clark and Matt Gardner. 2018 · 2018
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Learning to coordinate multiple reinforcement learning agents for diverse query reformulation
Rodrigo Nogueira, Jannis Bulian, and Massimiliano Ciaramita. 2018 · 2018
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Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, and Daniel Gildea. 2018 · 2018
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R3: Reinforced ranker-reader for open-domain question answering
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Evidence aggregation for answer re-ranking in open-domain question answering
Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, and Murray Campbell. 2018b · 2018
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Qanet: Combining local convolution with global self-attention for reading comprehension
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Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le. 2018 · 2018
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Learning to transform, combine, and reason in open-domain question answering
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