<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="das-etal-2022-one">
    <titleInfo>
        <title>Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Millon</namePart>
        <namePart type="family">Das</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Punyajoy</namePart>
        <namePart type="family">Saha</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mithun</namePart>
        <namePart type="family">Das</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ritesh</namePart>
            <namePart type="family">Kumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Atul</namePart>
            <namePart type="given">Kr.</namePart>
            <namePart type="family">Ojha</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marcos</namePart>
            <namePart type="family">Zampieri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shervin</namePart>
            <namePart type="family">Malmasi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Kadar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. Hence, few of the past works transform the same task into a regression. This paper shows the comparative evaluation of different transformers and traditional machine learning models on a recently released toxicity severity measurement dataset by Jigsaw. We further demonstrate the issues with the model predictions using explainability analysis.</abstract>
    <identifier type="citekey">das-etal-2022-one</identifier>
    <location>
        <url>https://aclanthology.org/2022.trac-1.2</url>
    </location>
    <part>
        <date>2022-10</date>
        <extent unit="page">
            <start>10</start>
            <end>15</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments
%A Das, Millon
%A Saha, Punyajoy
%A Das, Mithun
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%Y Kadar, Daniel
%S Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F das-etal-2022-one
%X The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. Hence, few of the past works transform the same task into a regression. This paper shows the comparative evaluation of different transformers and traditional machine learning models on a recently released toxicity severity measurement dataset by Jigsaw. We further demonstrate the issues with the model predictions using explainability analysis.
%U https://aclanthology.org/2022.trac-1.2
%P 10-15

Markdown (Informal)

[Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments](https://aclanthology.org/2022.trac-1.2) (Das et al., TRAC 2022)

• Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments (Das et al., TRAC 2022)

ACL

• Millon Das, Punyajoy Saha, and Mithun Das. 2022. Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments. In Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022), pages 10–15, Gyeongju, Republic of Korea. Association for Computational Linguistics.