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    <titleInfo>
        <title>It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction</title>
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    <name type="personal">
        <namePart type="given">Slavena</namePart>
        <namePart type="family">Vasileva</namePart>
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    <name type="personal">
        <namePart type="given">Pepa</namePart>
        <namePart type="family">Atanasova</namePart>
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    <name type="personal">
        <namePart type="given">Lluís</namePart>
        <namePart type="family">Màrquez</namePart>
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    <name type="personal">
        <namePart type="given">Alberto</namePart>
        <namePart type="family">Barrón-Cedeño</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Preslav</namePart>
        <namePart type="family">Nakov</namePart>
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        <dateIssued>2019-09</dateIssued>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.</abstract>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction
%A Vasileva, Slavena
%A Atanasova, Pepa
%A Màrquez, Lluís
%A Barrón-Cedeño, Alberto
%A Nakov, Preslav
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F vasileva-etal-2019-takes
%X We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.
%R 10.26615/978-954-452-056-4_141
%U https://aclanthology.org/R19-1141
%U https://doi.org/10.26615/978-954-452-056-4_141
%P 1229-1239

Markdown (Informal)

[It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction](https://aclanthology.org/R19-1141) (Vasileva et al., RANLP 2019)

• It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction (Vasileva et al., RANLP 2019)

ACL

• Slavena Vasileva, Pepa Atanasova, Lluís Màrquez, Alberto Barrón-Cedeño, and Preslav Nakov. 2019. It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1229–1239, Varna, Bulgaria. INCOMA Ltd..