<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="akazawa-etal-2023-distilling">
    <titleInfo>
        <title>Distilling Implied Bias from Hate Speech for Counter Narrative Selection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Nami</namePart>
        <namePart type="family">Akazawa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Serra</namePart>
        <namePart type="given">Sinem</namePart>
        <namePart type="family">Tekiroğlu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marco</namePart>
        <namePart type="family">Guerini</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 1st Workshop on CounterSpeech for Online Abuse (CS4OA)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yi-Ling</namePart>
            <namePart type="family">Chung</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Helena</namePart>
            <namePart type="family">Bonaldi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Gavin</namePart>
            <namePart type="family">Abercrombie</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marco</namePart>
            <namePart type="family">Guerini</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Prague, Czechia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Hate speech is a critical problem in our society and social media platforms are often an amplifier for this phenomenon. Recently the use of Counter Narratives (informative and non-aggressive responses) has been proposed as a viable solution to counter hateful content that goes beyond simple detection-removal strategies. In this paper we present a novel approach along this line of research, which utilizes the implied statement (bias) expressed in the hate speech to retrieve an appropriate counter narrative. To this end, we first trained and tested several LMs that, given a hateful post, generate the underlying bias and the target group. Then, for the counter narrative selection task, we experimented with several methodologies that either use or not use the implied bias during the process. Experiments show that using the target group information allows the system to better focus on relevant content and that implied statement for selecting counter narratives is better than the corresponding standard approach that does not use it. To our knowledge, this is the first attempt to build an automatic selection tool that uses hate speech implied bias to drive Counter Narrative selection.</abstract>
    <identifier type="citekey">akazawa-etal-2023-distilling</identifier>
    <location>
        <url>https://aclanthology.org/2023.cs4oa-1.3</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>29</start>
            <end>43</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Distilling Implied Bias from Hate Speech for Counter Narrative Selection
%A Akazawa, Nami
%A Tekiroğlu, Serra Sinem
%A Guerini, Marco
%Y Chung, Yi-Ling
%Y Bonaldi, Helena
%Y Abercrombie, Gavin
%Y Guerini, Marco
%S Proceedings of the 1st Workshop on CounterSpeech for Online Abuse (CS4OA)
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F akazawa-etal-2023-distilling
%X Hate speech is a critical problem in our society and social media platforms are often an amplifier for this phenomenon. Recently the use of Counter Narratives (informative and non-aggressive responses) has been proposed as a viable solution to counter hateful content that goes beyond simple detection-removal strategies. In this paper we present a novel approach along this line of research, which utilizes the implied statement (bias) expressed in the hate speech to retrieve an appropriate counter narrative. To this end, we first trained and tested several LMs that, given a hateful post, generate the underlying bias and the target group. Then, for the counter narrative selection task, we experimented with several methodologies that either use or not use the implied bias during the process. Experiments show that using the target group information allows the system to better focus on relevant content and that implied statement for selecting counter narratives is better than the corresponding standard approach that does not use it. To our knowledge, this is the first attempt to build an automatic selection tool that uses hate speech implied bias to drive Counter Narrative selection.
%U https://aclanthology.org/2023.cs4oa-1.3
%P 29-43

Markdown (Informal)

[Distilling Implied Bias from Hate Speech for Counter Narrative Selection](https://aclanthology.org/2023.cs4oa-1.3) (Akazawa et al., CS4OA-WS 2023)

• Distilling Implied Bias from Hate Speech for Counter Narrative Selection (Akazawa et al., CS4OA-WS 2023)

ACL

• Nami Akazawa, Serra Sinem Tekiroğlu, and Marco Guerini. 2023. Distilling Implied Bias from Hate Speech for Counter Narrative Selection. In Proceedings of the 1st Workshop on CounterSpeech for Online Abuse (CS4OA), pages 29–43, Prague, Czechia. Association for Computational Linguistics.