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    <titleInfo>
        <title>Harvesting Paragraph-level Question-Answer Pairs from Wikipedia</title>
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    <name type="personal">
        <namePart type="given">Xinya</namePart>
        <namePart type="family">Du</namePart>
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    <name type="personal">
        <namePart type="given">Claire</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>
            <role>
                <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>We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. As compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top ranking Wikipedia articles and create a corpus of over one million question-answer pairs. We provide qualitative analysis for the this large-scale generated corpus from Wikipedia.</abstract>
    <identifier type="citekey">du-cardie-2018-harvesting</identifier>
    <identifier type="doi">10.18653/v1/P18-1177</identifier>
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        <url>https://aclanthology.org/P18-1177</url>
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    <part>
        <date>2018-07</date>
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            <start>1907</start>
            <end>1917</end>
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%0 Conference Proceedings
%T Harvesting Paragraph-level Question-Answer Pairs from Wikipedia
%A Du, Xinya
%A Cardie, Claire
%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 du-cardie-2018-harvesting
%X We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. As compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top ranking Wikipedia articles and create a corpus of over one million question-answer pairs. We provide qualitative analysis for the this large-scale generated corpus from Wikipedia.
%R 10.18653/v1/P18-1177
%U https://aclanthology.org/P18-1177
%U https://doi.org/10.18653/v1/P18-1177
%P 1907-1917

Markdown (Informal)

[Harvesting Paragraph-level Question-Answer Pairs from Wikipedia](https://aclanthology.org/P18-1177) (Du & Cardie, ACL 2018)

• Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (Du & Cardie, ACL 2018)

ACL

• Xinya Du and Claire Cardie. 2018. Harvesting Paragraph-level Question-Answer Pairs from Wikipedia. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1907–1917, Melbourne, Australia. Association for Computational Linguistics.