@inproceedings{calvo-figueras-etal-2022-semantics,
title = "A Semantics-Aware Approach to Automated Claim Verification",
author = "Calvo Figueras, Blanca and
Oller, Montse and
Agerri, Rodrigo",
booktitle = "Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.fever-1.5",
doi = "10.18653/v1/2022.fever-1.5",
pages = "37--48",
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.",
}
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%0 Conference Proceedings
%T A Semantics-Aware Approach to Automated Claim Verification
%A Calvo Figueras, Blanca
%A Oller, Montse
%A Agerri, Rodrigo
%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)
ACL