November 4, 2019
We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agents to select the passage sentences that most convince a pretrained QA model of a given answer, if the QA model received those sentences instead of the full passage. Rather than finding evidence that convinces one model alone, we find that agents select evidence that generalizes; agent-chosen evidence increases the plausibility of the supported answer, as judged by other QA models and humans. Given its general nature, this approach improves QA in a robust manner: using agent-selected evidence (i) humans can correctly answer questions with only ∼20% of the full passage and (ii) QA models can generalize to longer passages and harder questions.
September 10, 2019
Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin
September 10, 2019
May 17, 2019
Jean Alaux, Edouard Grave, Marco Cuturi, Armand Joulin
May 17, 2019
July 27, 2019
Patrick Lewis, Ludovic Denoyer, Sebastian Riedel
July 27, 2019
August 01, 2019
Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
August 01, 2019
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