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        <title>Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts</title>
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        <namePart type="given">Jong-Hoon</namePart>
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    <name type="personal">
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    <abstract>In this paper, we propose a method for why-question answering (why-QA) that uses an adversarial learning framework. Existing why-QA methods retrieve “answer passages” that usually consist of several sentences. These multi-sentence passages contain not only the reason sought by a why-question and its connection to the why-question, but also redundant and/or unrelated parts. We use our proposed “Adversarial networks for Generating compact-answer Representation” (AGR) to generate from a passage a vector representation of the non-redundant reason sought by a why-question and exploit the representation for judging whether the passage actually answers the why-question. Through a series of experiments using Japanese why-QA datasets, we show that these representations improve the performance of our why-QA neural model as well as that of a BERT-based why-QA model. We show that they also improve a state-of-the-art distantly supervised open-domain QA (DS-QA) method on publicly available English datasets, even though the target task is not a why-QA.</abstract>
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%0 Conference Proceedings
%T Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts
%A Oh, Jong-Hoon
%A Kadowaki, Kazuma
%A Kloetzer, Julien
%A Iida, Ryu
%A Torisawa, Kentaro
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F oh-etal-2019-open
%X In this paper, we propose a method for why-question answering (why-QA) that uses an adversarial learning framework. Existing why-QA methods retrieve “answer passages” that usually consist of several sentences. These multi-sentence passages contain not only the reason sought by a why-question and its connection to the why-question, but also redundant and/or unrelated parts. We use our proposed “Adversarial networks for Generating compact-answer Representation” (AGR) to generate from a passage a vector representation of the non-redundant reason sought by a why-question and exploit the representation for judging whether the passage actually answers the why-question. Through a series of experiments using Japanese why-QA datasets, we show that these representations improve the performance of our why-QA neural model as well as that of a BERT-based why-QA model. We show that they also improve a state-of-the-art distantly supervised open-domain QA (DS-QA) method on publicly available English datasets, even though the target task is not a why-QA.
%R 10.18653/v1/P19-1414
%U https://aclanthology.org/P19-1414
%U https://doi.org/10.18653/v1/P19-1414
%P 4227-4237

Markdown (Informal)

[Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts](https://aclanthology.org/P19-1414) (Oh et al., ACL 2019)

• Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts (Oh et al., ACL 2019)

ACL

• Jong-Hoon Oh, Kazuma Kadowaki, Julien Kloetzer, Ryu Iida, and Kentaro Torisawa. 2019. Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4227–4237, Florence, Italy. Association for Computational Linguistics.