@inproceedings{perez-etal-2019-finding,
title = "Finding Generalizable Evidence by Learning to Convince {Q}{\&}{A} Models",
author = "Perez, Ethan and
Karamcheti, Siddharth and
Fergus, Rob and
Weston, Jason and
Kiela, Douwe and
Cho, Kyunghyun",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1244",
doi = "10.18653/v1/D19-1244",
pages = "2402--2411",
abstract = "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 {\textasciitilde}20{\%} of the full passage and (ii) QA models can generalize to longer passages and harder questions.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Finding Generalizable Evidence by Learning to Convince Q&A Models
%A Perez, Ethan
%A Karamcheti, Siddharth
%A Fergus, Rob
%A Weston, Jason
%A Kiela, Douwe
%A Cho, Kyunghyun
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F perez-etal-2019-finding
%X 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.
%R 10.18653/v1/D19-1244
%U https://aclanthology.org/D19-1244
%U https://doi.org/10.18653/v1/D19-1244
%P 2402-2411
Markdown (Informal)
[Finding Generalizable Evidence by Learning to Convince Q&A Models](https://aclanthology.org/D19-1244) (Perez et al., EMNLP-IJCNLP 2019)
ACL
- Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, and Kyunghyun Cho. 2019. Finding Generalizable Evidence by Learning to Convince Q&A Models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2402–2411, Hong Kong, China. Association for Computational Linguistics.