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The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer.
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
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End-to-End Open-Domain Question Answering with BERTserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019a
Cited in the paper.
Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, and Jing Jiang. 2018 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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