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    <abstract>This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA). The problem aims to efficiently find a small set of passages that covers full evidence from multiple aspects as to answer a complex question. To this end, we proposes a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages. Our experiments demonstrate that our method considers the dependence within the supporting evidence and significantly improves the accuracy of complementary evidence selection in QA domain.</abstract>
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%0 Conference Proceedings
%T Complementary Evidence Identification in Open-Domain Question Answering
%A Mou, Xiangyang
%A Yu, Mo
%A Chang, Shiyu
%A Feng, Yufei
%A Zhang, Li
%A Su, Hui
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F mou-etal-2021-complementary
%X This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA). The problem aims to efficiently find a small set of passages that covers full evidence from multiple aspects as to answer a complex question. To this end, we proposes a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages. Our experiments demonstrate that our method considers the dependence within the supporting evidence and significantly improves the accuracy of complementary evidence selection in QA domain.
%R 10.18653/v1/2021.eacl-main.234
%U https://aclanthology.org/2021.eacl-main.234
%U https://doi.org/10.18653/v1/2021.eacl-main.234
%P 2720-2726

Markdown (Informal)

[Complementary Evidence Identification in Open-Domain Question Answering](https://aclanthology.org/2021.eacl-main.234) (Mou et al., EACL 2021)

• Complementary Evidence Identification in Open-Domain Question Answering (Mou et al., EACL 2021)

ACL

• Xiangyang Mou, Mo Yu, Shiyu Chang, Yufei Feng, Li Zhang, and Hui Su. 2021. Complementary Evidence Identification in Open-Domain Question Answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2720–2726, Online. Association for Computational Linguistics.