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        <title>Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks</title>
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    <name type="personal">
        <namePart type="given">Christopher</namePart>
        <namePart type="family">Hidey</namePart>
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    <name type="personal">
        <namePart type="given">Mona</namePart>
        <namePart type="family">Diab</namePart>
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            <title>Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)</title>
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        <name type="personal">
            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
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        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Oana</namePart>
            <namePart type="family">Cocarascu</namePart>
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        <name type="personal">
            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Arpit</namePart>
            <namePart type="family">Mittal</namePart>
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    <abstract>Many tasks such as question answering and reading comprehension rely on information extracted from unreliable sources. These systems would thus benefit from knowing whether a statement from an unreliable source is correct. We present experiments on the FEVER (Fact Extraction and VERification) task, a shared task that involves selecting sentences from Wikipedia and predicting whether a claim is supported by those sentences, refuted, or there is not enough information. Fact checking is a task that benefits from not only asserting or disputing the veracity of a claim but also finding evidence for that position. As these tasks are dependent on each other, an ideal model would consider the veracity of the claim when finding evidence and also find only the evidence that is relevant. We thus jointly model sentence extraction and verification on the FEVER shared task. Among all participants, we ranked 5th on the blind test set (prior to any additional human evaluation of the evidence).</abstract>
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    <identifier type="doi">10.18653/v1/W18-5525</identifier>
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        <date>2018-11</date>
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%0 Conference Proceedings
%T Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks
%A Hidey, Christopher
%A Diab, Mona
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F hidey-diab-2018-team
%X Many tasks such as question answering and reading comprehension rely on information extracted from unreliable sources. These systems would thus benefit from knowing whether a statement from an unreliable source is correct. We present experiments on the FEVER (Fact Extraction and VERification) task, a shared task that involves selecting sentences from Wikipedia and predicting whether a claim is supported by those sentences, refuted, or there is not enough information. Fact checking is a task that benefits from not only asserting or disputing the veracity of a claim but also finding evidence for that position. As these tasks are dependent on each other, an ideal model would consider the veracity of the claim when finding evidence and also find only the evidence that is relevant. We thus jointly model sentence extraction and verification on the FEVER shared task. Among all participants, we ranked 5th on the blind test set (prior to any additional human evaluation of the evidence).
%R 10.18653/v1/W18-5525
%U https://aclanthology.org/W18-5525
%U https://doi.org/10.18653/v1/W18-5525
%P 150-155

Markdown (Informal)

[Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks](https://aclanthology.org/W18-5525) (Hidey & Diab, EMNLP 2018)

• Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks (Hidey & Diab, EMNLP 2018)

ACL

• Christopher Hidey and Mona Diab. 2018. Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 150–155, Brussels, Belgium. Association for Computational Linguistics.