<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chang-etal-2026-beyond">
    <titleInfo>
        <title>Beyond Generic Responses: Target-Aware Strategies for Countering Hate Speech</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yen-Yu</namePart>
        <namePart type="family">Chang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Daryna</namePart>
        <namePart type="family">Dementieva</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Fraser</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Krek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Toral</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resource Association</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Effective counter-narratives (CNs) are essential for combating online hate speech, yet generic responses often fail to address the specific needs of targeted groups. This paper proposes a target-aware CN generation framework that incorporates demographic-specific tokens into transformer-based models. Our approach enhances the contextual relevance by introducing target-group tokens into the model’s vocabulary. To assess CN quality, we employ a multifaceted evaluation framework, including automatic metrics and LLM as Judges (JudgeLM). Evaluation with a wide range of language models demonstrates that target group tokens markedly improve contextual relevance of generated CN, particularly in small and medium models, with measurable gains in validity as CN and contextual relevance. Even for large instruction-tuned models, such as LLaMA-3, incorporating target-specific information proves effective in enhancing contextual relevance of generated responses. Warning: This paper contains offensive texts that are only used for combating online hate.</abstract>
    <identifier type="citekey">chang-etal-2026-beyond</identifier>
    <identifier type="doi">10.63317/2mhddxjwnbmu</identifier>
    <location>
        <url>https://aclanthology.org/2026.lrec-1.1/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>1</start>
            <end>18</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Beyond Generic Responses: Target-Aware Strategies for Countering Hate Speech
%A Chang, Yen-Yu
%A Dementieva, Daryna
%A Fraser, Alexander
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F chang-etal-2026-beyond
%X Effective counter-narratives (CNs) are essential for combating online hate speech, yet generic responses often fail to address the specific needs of targeted groups. This paper proposes a target-aware CN generation framework that incorporates demographic-specific tokens into transformer-based models. Our approach enhances the contextual relevance by introducing target-group tokens into the model’s vocabulary. To assess CN quality, we employ a multifaceted evaluation framework, including automatic metrics and LLM as Judges (JudgeLM). Evaluation with a wide range of language models demonstrates that target group tokens markedly improve contextual relevance of generated CN, particularly in small and medium models, with measurable gains in validity as CN and contextual relevance. Even for large instruction-tuned models, such as LLaMA-3, incorporating target-specific information proves effective in enhancing contextual relevance of generated responses. Warning: This paper contains offensive texts that are only used for combating online hate.
%R 10.63317/2mhddxjwnbmu
%U https://aclanthology.org/2026.lrec-1.1/
%U https://doi.org/10.63317/2mhddxjwnbmu
%P 1-18

Markdown (Informal)

[Beyond Generic Responses: Target-Aware Strategies for Countering Hate Speech](https://aclanthology.org/2026.lrec-1.1/) (Chang et al., LREC 2026)

• Beyond Generic Responses: Target-Aware Strategies for Countering Hate Speech (Chang et al., LREC 2026)

ACL

• Yen-Yu Chang, Daryna Dementieva, and Alexander Fraser. 2026. Beyond Generic Responses: Target-Aware Strategies for Countering Hate Speech. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1–18, Palma de Mallorca, Spain. ELRA Language Resource Association.