<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="portelli-etal-2020-distilling">
    <titleInfo>
        <title>Distilling the Evidence to Augment Fact Verification Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Beatrice</namePart>
        <namePart type="family">Portelli</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jason</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tal</namePart>
        <namePart type="family">Schuster</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Giuseppe</namePart>
        <namePart type="family">Serra</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Enrico</namePart>
        <namePart type="family">Santus</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Oana</namePart>
            <namePart type="family">Cocarascu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Arpit</namePart>
            <namePart type="family">Mittal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the development of natural language processing techniques to automatically verify the veracity of claims. Most approaches perform a claim-evidence classification without providing any insights about why the claim is trustworthy or not. We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim. We show that the spans are informative for the classifier, improving performance and robustness. Tested on several state-of-the-art models over the Fever dataset, the enhanced classifiers consistently achieve higher accuracy while also showing reduced sensitivity to artifacts in the claims.</abstract>
    <identifier type="citekey">portelli-etal-2020-distilling</identifier>
    <identifier type="doi">10.18653/v1/2020.fever-1.7</identifier>
    <location>
        <url>https://aclanthology.org/2020.fever-1.7</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>47</start>
            <end>51</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Distilling the Evidence to Augment Fact Verification Models
%A Portelli, Beatrice
%A Zhao, Jason
%A Schuster, Tal
%A Serra, Giuseppe
%A Santus, Enrico
%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 portelli-etal-2020-distilling
%X The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the development of natural language processing techniques to automatically verify the veracity of claims. Most approaches perform a claim-evidence classification without providing any insights about why the claim is trustworthy or not. We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim. We show that the spans are informative for the classifier, improving performance and robustness. Tested on several state-of-the-art models over the Fever dataset, the enhanced classifiers consistently achieve higher accuracy while also showing reduced sensitivity to artifacts in the claims.
%R 10.18653/v1/2020.fever-1.7
%U https://aclanthology.org/2020.fever-1.7
%U https://doi.org/10.18653/v1/2020.fever-1.7
%P 47-51

Markdown (Informal)

[Distilling the Evidence to Augment Fact Verification Models](https://aclanthology.org/2020.fever-1.7) (Portelli et al., FEVER 2020)

• Distilling the Evidence to Augment Fact Verification Models (Portelli et al., FEVER 2020)

ACL

• Beatrice Portelli, Jason Zhao, Tal Schuster, Giuseppe Serra, and Enrico Santus. 2020. Distilling the Evidence to Augment Fact Verification Models. In Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER), pages 47–51, Online. Association for Computational Linguistics.