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        <title>Automated Fact-Checking of Claims in Argumentative Parliamentary Debates</title>
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            <title>Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)</title>
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            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
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    <abstract>We present an automated approach to distinguish true, false, stretch, and dodge statements in questions and answers in the Canadian Parliament. We leverage the truthfulness annotations of a U.S. fact-checking corpus by training a neural net model and incorporating the prediction probabilities into our models. We find that in concert with other linguistic features, these probabilities can improve the multi-class classification results. We further show that dodge statements can be detected with an F1 measure as high as 82.57% in binary classification settings.</abstract>
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    <identifier type="doi">10.18653/v1/W18-5509</identifier>
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%0 Conference Proceedings
%T Automated Fact-Checking of Claims in Argumentative Parliamentary Debates
%A Naderi, Nona
%A Hirst, Graeme
%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 naderi-hirst-2018-automated
%X We present an automated approach to distinguish true, false, stretch, and dodge statements in questions and answers in the Canadian Parliament. We leverage the truthfulness annotations of a U.S. fact-checking corpus by training a neural net model and incorporating the prediction probabilities into our models. We find that in concert with other linguistic features, these probabilities can improve the multi-class classification results. We further show that dodge statements can be detected with an F1 measure as high as 82.57% in binary classification settings.
%R 10.18653/v1/W18-5509
%U https://aclanthology.org/W18-5509
%U https://doi.org/10.18653/v1/W18-5509
%P 60-65

Markdown (Informal)

[Automated Fact-Checking of Claims in Argumentative Parliamentary Debates](https://aclanthology.org/W18-5509) (Naderi & Hirst, EMNLP 2018)

• Automated Fact-Checking of Claims in Argumentative Parliamentary Debates (Naderi & Hirst, EMNLP 2018)

ACL

• Nona Naderi and Graeme Hirst. 2018. Automated Fact-Checking of Claims in Argumentative Parliamentary Debates. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 60–65, Brussels, Belgium. Association for Computational Linguistics.