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    <titleInfo>
        <title>Leveraging Context Information for Natural Question Generation</title>
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    <name type="personal">
        <namePart type="given">Linfeng</namePart>
        <namePart type="family">Song</namePart>
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    <name type="personal">
        <namePart type="given">Zhiguo</namePart>
        <namePart type="family">Wang</namePart>
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    <name type="personal">
        <namePart type="given">Wael</namePart>
        <namePart type="family">Hamza</namePart>
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    <name type="personal">
        <namePart type="given">Yue</namePart>
        <namePart type="family">Zhang</namePart>
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    <name type="personal">
        <namePart type="given">Daniel</namePart>
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            <title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)</title>
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            <namePart type="given">Marilyn</namePart>
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        <name type="personal">
            <namePart type="given">Heng</namePart>
            <namePart type="family">Ji</namePart>
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        <name type="personal">
            <namePart type="given">Amanda</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.</abstract>
    <identifier type="citekey">song-etal-2018-leveraging</identifier>
    <identifier type="doi">10.18653/v1/N18-2090</identifier>
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        <url>https://aclanthology.org/N18-2090</url>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Leveraging Context Information for Natural Question Generation
%A Song, Linfeng
%A Wang, Zhiguo
%A Hamza, Wael
%A Zhang, Yue
%A Gildea, Daniel
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F song-etal-2018-leveraging
%X The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.
%R 10.18653/v1/N18-2090
%U https://aclanthology.org/N18-2090
%U https://doi.org/10.18653/v1/N18-2090
%P 569-574

Markdown (Informal)

[Leveraging Context Information for Natural Question Generation](https://aclanthology.org/N18-2090) (Song et al., NAACL 2018)

• Leveraging Context Information for Natural Question Generation (Song et al., NAACL 2018)

ACL

• Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, and Daniel Gildea. 2018. Leveraging Context Information for Natural Question Generation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 569–574, New Orleans, Louisiana. Association for Computational Linguistics.