<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zou-etal-2023-decker">
    <titleInfo>
        <title>Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Anni</namePart>
        <namePart type="family">Zou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhuosheng</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hai</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: ACL 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jordan</namePart>
            <namePart type="family">Boyd-Graber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Naoaki</namePart>
            <namePart type="family">Okazaki</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Commonsense fact verification, as a challenging branch of commonsense question-answering (QA), aims to verify through facts whether a given commonsense claim is correct or not. Answering commonsense questions necessitates a combination of knowledge from various levels. However, existing studies primarily rest on grasping either unstructured evidence or potential reasoning paths from structured knowledge bases, yet failing to exploit the benefits of heterogeneous knowledge simultaneously. In light of this, we propose Decker, a commonsense fact verification model that is capable of bridging heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. Experimental results on two commonsense fact verification benchmark datasets, CSQA2.0 and CREAK demonstrate the effectiveness of our Decker and further analysis verifies its capability to seize more precious information through reasoning. The official implementation of Decker is available at https://github.com/Anni-Zou/Decker.</abstract>
    <identifier type="citekey">zou-etal-2023-decker</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-acl.752</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-acl.752</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>11891</start>
            <end>11904</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification
%A Zou, Anni
%A Zhang, Zhuosheng
%A Zhao, Hai
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zou-etal-2023-decker
%X Commonsense fact verification, as a challenging branch of commonsense question-answering (QA), aims to verify through facts whether a given commonsense claim is correct or not. Answering commonsense questions necessitates a combination of knowledge from various levels. However, existing studies primarily rest on grasping either unstructured evidence or potential reasoning paths from structured knowledge bases, yet failing to exploit the benefits of heterogeneous knowledge simultaneously. In light of this, we propose Decker, a commonsense fact verification model that is capable of bridging heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. Experimental results on two commonsense fact verification benchmark datasets, CSQA2.0 and CREAK demonstrate the effectiveness of our Decker and further analysis verifies its capability to seize more precious information through reasoning. The official implementation of Decker is available at https://github.com/Anni-Zou/Decker.
%R 10.18653/v1/2023.findings-acl.752
%U https://aclanthology.org/2023.findings-acl.752
%U https://doi.org/10.18653/v1/2023.findings-acl.752
%P 11891-11904

Markdown (Informal)

[Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification](https://aclanthology.org/2023.findings-acl.752) (Zou et al., Findings 2023)

• Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification (Zou et al., Findings 2023)

ACL

• Anni Zou, Zhuosheng Zhang, and Hai Zhao. 2023. Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11891–11904, Toronto, Canada. Association for Computational Linguistics.