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        <title>Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information</title>
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    <name type="personal">
        <namePart type="given">Sudha</namePart>
        <namePart type="family">Rao</namePart>
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        <namePart type="given">Hal</namePart>
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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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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clarification questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clarification questions consisting of 77K posts paired with a clarification question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate significant improvements over controlled baselines.</abstract>
    <identifier type="citekey">rao-daume-iii-2018-learning</identifier>
    <identifier type="doi">10.18653/v1/P18-1255</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
%A Rao, Sudha
%A Daumé III, Hal
%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 rao-daume-iii-2018-learning
%X Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clarification questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clarification questions consisting of 77K posts paired with a clarification question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate significant improvements over controlled baselines.
%R 10.18653/v1/P18-1255
%U https://aclanthology.org/P18-1255
%U https://doi.org/10.18653/v1/P18-1255
%P 2737-2746

Markdown (Informal)

[Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://aclanthology.org/P18-1255) (Rao & Daumé III, ACL 2018)

• Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information (Rao & Daumé III, ACL 2018)

ACL

• Sudha Rao and Hal Daumé III. 2018. Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2737–2746, Melbourne, Australia. Association for Computational Linguistics.