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    <abstract>The influence of fake news in the perception of reality has become a mainstream topic in the last years due to the fast propagation of misleading information. In order to help in the fight against misinformation, automated solutions to fact-checking are being actively developed within the research community. In this context, the task of Automated Claim Verification is defined as assessing the truthfulness of a claim by finding evidence about its veracity. In this work we empirically demonstrate that enriching a BERT model with explicit semantic information such as Semantic Role Labelling helps to improve results in claim verification as proposed by the FEVER benchmark. Furthermore, we perform a number of explainability tests that suggest that the semantically-enriched model is better at handling complex cases, such as those including passive forms or multiple propositions.</abstract>
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%0 Conference Proceedings
%T A Semantics-Aware Approach to Automated Claim Verification
%A Calvo Figueras, Blanca
%A Cuadros, Montse
%A Agerri, Rodrigo
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F calvo-figueras-etal-2022-semantics
%X The influence of fake news in the perception of reality has become a mainstream topic in the last years due to the fast propagation of misleading information. In order to help in the fight against misinformation, automated solutions to fact-checking are being actively developed within the research community. In this context, the task of Automated Claim Verification is defined as assessing the truthfulness of a claim by finding evidence about its veracity. In this work we empirically demonstrate that enriching a BERT model with explicit semantic information such as Semantic Role Labelling helps to improve results in claim verification as proposed by the FEVER benchmark. Furthermore, we perform a number of explainability tests that suggest that the semantically-enriched model is better at handling complex cases, such as those including passive forms or multiple propositions.
%R 10.18653/v1/2022.fever-1.5
%U https://aclanthology.org/2022.fever-1.5
%U https://doi.org/10.18653/v1/2022.fever-1.5
%P 37-48

Markdown (Informal)

[A Semantics-Aware Approach to Automated Claim Verification](https://aclanthology.org/2022.fever-1.5) (Calvo Figueras et al., FEVER 2022)

• A Semantics-Aware Approach to Automated Claim Verification (Calvo Figueras et al., FEVER 2022)

ACL

• Blanca Calvo Figueras, Montse Cuadros, and Rodrigo Agerri. 2022. A Semantics-Aware Approach to Automated Claim Verification. In Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER), pages 37–48, Dublin, Ireland. Association for Computational Linguistics.