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        <title>Question Condensing Networks for Answer Selection in Community Question Answering</title>
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        <namePart type="given">Wei</namePart>
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        <namePart type="family">Sun</namePart>
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    <name type="personal">
        <namePart type="given">Houfeng</namePart>
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        <dateIssued>2018-07</dateIssued>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Answer selection is an important subtask of community question answering (CQA). In a real-world CQA forum, a question is often represented as two parts: a subject that summarizes the main points of the question, and a body that elaborates on the subject in detail. Previous researches on answer selection usually ignored the difference between these two parts and concatenated them as the question representation. In this paper, we propose the Question Condensing Networks (QCN) to make use of the subject-body relationship of community questions. In our model, the question subject is the primary part of the question representation, and the question body information is aggregated based on similarity and disparity with the question subject. Experimental results show that QCN outperforms all existing models on two CQA datasets.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1162</identifier>
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%0 Conference Proceedings
%T Question Condensing Networks for Answer Selection in Community Question Answering
%A Wu, Wei
%A Sun, Xu
%A Wang, Houfeng
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F wu-etal-2018-question
%X Answer selection is an important subtask of community question answering (CQA). In a real-world CQA forum, a question is often represented as two parts: a subject that summarizes the main points of the question, and a body that elaborates on the subject in detail. Previous researches on answer selection usually ignored the difference between these two parts and concatenated them as the question representation. In this paper, we propose the Question Condensing Networks (QCN) to make use of the subject-body relationship of community questions. In our model, the question subject is the primary part of the question representation, and the question body information is aggregated based on similarity and disparity with the question subject. Experimental results show that QCN outperforms all existing models on two CQA datasets.
%R 10.18653/v1/P18-1162
%U https://aclanthology.org/P18-1162
%U https://doi.org/10.18653/v1/P18-1162
%P 1746-1755

Markdown (Informal)

[Question Condensing Networks for Answer Selection in Community Question Answering](https://aclanthology.org/P18-1162) (Wu et al., ACL 2018)

• Question Condensing Networks for Answer Selection in Community Question Answering (Wu et al., ACL 2018)

ACL

• Wei Wu, Xu Sun, and Houfeng Wang. 2018. Question Condensing Networks for Answer Selection in Community Question Answering. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1746–1755, Melbourne, Australia. Association for Computational Linguistics.