<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="calvo-figueras-etal-2022-semantics">
    <titleInfo>
        <title>A Semantics-Aware Approach to Automated Claim Verification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Blanca</namePart>
        <namePart type="family">Calvo Figueras</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Montse</namePart>
        <namePart type="family">Oller</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rodrigo</namePart>
        <namePart type="family">Agerri</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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>
    <identifier type="citekey">calvo-figueras-etal-2022-semantics</identifier>
    <identifier type="doi">10.18653/v1/2022.fever-1.5</identifier>
    <location>
        <url>https://aclanthology.org/2022.fever-1.5</url>
    </location>
    <part>
        <date>2022-05</date>
        <extent unit="page">
            <start>37</start>
            <end>48</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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)

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

ACL

• Blanca Calvo Figueras, Montse Oller, 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.