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        <title>Where is Your Evidence: Improving Fact-checking by Justification Modeling</title>
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    <name type="personal">
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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">
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    <abstract>Fact-checking is a journalistic practice that compares a claim made publicly against trusted sources of facts. Wang (2017) introduced a large dataset of validated claims from the POLITIFACT.com website (LIAR dataset), enabling the development of machine learning approaches for fact-checking. However, approaches based on this dataset have focused primarily on modeling the claim and speaker-related metadata, without considering the evidence used by humans in labeling the claims. We extend the LIAR dataset by automatically extracting the justification from the fact-checking article used by humans to label a given claim. We show that modeling the extracted justification in conjunction with the claim (and metadata) provides a significant improvement regardless of the machine learning model used (feature-based or deep learning) both in a binary classification task (true, false) and in a six-way classification task (pants on fire, false, mostly false, half true, mostly true, true).</abstract>
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    <identifier type="doi">10.18653/v1/W18-5513</identifier>
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%0 Conference Proceedings
%T Where is Your Evidence: Improving Fact-checking by Justification Modeling
%A Alhindi, Tariq
%A Petridis, Savvas
%A Muresan, Smaranda
%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 alhindi-etal-2018-evidence
%X Fact-checking is a journalistic practice that compares a claim made publicly against trusted sources of facts. Wang (2017) introduced a large dataset of validated claims from the POLITIFACT.com website (LIAR dataset), enabling the development of machine learning approaches for fact-checking. However, approaches based on this dataset have focused primarily on modeling the claim and speaker-related metadata, without considering the evidence used by humans in labeling the claims. We extend the LIAR dataset by automatically extracting the justification from the fact-checking article used by humans to label a given claim. We show that modeling the extracted justification in conjunction with the claim (and metadata) provides a significant improvement regardless of the machine learning model used (feature-based or deep learning) both in a binary classification task (true, false) and in a six-way classification task (pants on fire, false, mostly false, half true, mostly true, true).
%R 10.18653/v1/W18-5513
%U https://aclanthology.org/W18-5513
%U https://doi.org/10.18653/v1/W18-5513
%P 85-90

Markdown (Informal)

[Where is Your Evidence: Improving Fact-checking by Justification Modeling](https://aclanthology.org/W18-5513) (Alhindi et al., EMNLP 2018)

• Where is Your Evidence: Improving Fact-checking by Justification Modeling (Alhindi et al., EMNLP 2018)

ACL

• Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018. Where is Your Evidence: Improving Fact-checking by Justification Modeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 85–90, Brussels, Belgium. Association for Computational Linguistics.