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        <title>Stance Prediction and Claim Verification: An Arabic Perspective</title>
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            <title>Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)</title>
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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        <name type="personal">
            <namePart type="given">James</namePart>
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        <name type="personal">
            <namePart type="given">Oana</namePart>
            <namePart type="family">Cocarascu</namePart>
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    <abstract>This work explores the application of textual entailment in news claim verification and stance prediction using a new corpus in Arabic. The publicly available corpus comes in two perspectives: a version consisting of 4,547 true and false claims and a version consisting of 3,786 pairs (claim, evidence). We describe the methodology for creating the corpus and the annotation process. Using the introduced corpus, we also develop two machine learning baselines for two proposed tasks: claim verification and stance prediction. Our best model utilizes pretraining (BERT) and achieves 76.7 F1 on the stance prediction task and 64.3 F1 on the claim verification task. Our preliminary experiments shed some light on the limits of automatic claim verification that relies on claims text only. Results hint that while the linguistic features and world knowledge learned during pretraining are useful for stance prediction, such learned representations from pretraining are insufficient for verifying claims without access to context or evidence.</abstract>
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    <identifier type="doi">10.18653/v1/2020.fever-1.2</identifier>
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%0 Conference Proceedings
%T Stance Prediction and Claim Verification: An Arabic Perspective
%A Khouja, Jude
%Y Christodoulopoulos, Christos
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Mittal, Arpit
%S Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F khouja-2020-stance
%X This work explores the application of textual entailment in news claim verification and stance prediction using a new corpus in Arabic. The publicly available corpus comes in two perspectives: a version consisting of 4,547 true and false claims and a version consisting of 3,786 pairs (claim, evidence). We describe the methodology for creating the corpus and the annotation process. Using the introduced corpus, we also develop two machine learning baselines for two proposed tasks: claim verification and stance prediction. Our best model utilizes pretraining (BERT) and achieves 76.7 F1 on the stance prediction task and 64.3 F1 on the claim verification task. Our preliminary experiments shed some light on the limits of automatic claim verification that relies on claims text only. Results hint that while the linguistic features and world knowledge learned during pretraining are useful for stance prediction, such learned representations from pretraining are insufficient for verifying claims without access to context or evidence.
%R 10.18653/v1/2020.fever-1.2
%U https://aclanthology.org/2020.fever-1.2
%U https://doi.org/10.18653/v1/2020.fever-1.2
%P 8-17

Markdown (Informal)

[Stance Prediction and Claim Verification: An Arabic Perspective](https://aclanthology.org/2020.fever-1.2) (Khouja, FEVER 2020)

• Stance Prediction and Claim Verification: An Arabic Perspective (Khouja, FEVER 2020)

ACL

• Jude Khouja. 2020. Stance Prediction and Claim Verification: An Arabic Perspective. In Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER), pages 8–17, Online. Association for Computational Linguistics.