@inproceedings{marquez-etal-2025-nlp,
title = "{NLP}@{IIMAS}-{CLTL} at Multilingual Counterspeech Generation: Combating Hate Speech Using Contextualized Knowledge Graph Representations and {LLM}s",
author = "M{\'a}rquez, David Salvador and
G{\'o}mez Adorno, Helena Montserrat and
Markov, Ilia and
B{\'a}ez Santamar{\'i}a, Selene",
editor = "Bonaldi, Helena and
Vallecillo-Rodr{\'i}guez, Mar{\'i}a Estrella and
Zubiaga, Irune and
Montejo-R{\'a}ez, Arturo and
Soroa, Aitor and
Mart{\'i}n-Valdivia, Mar{\'i}a Teresa and
Guerini, Marco and
Agerri, Rodrigo",
booktitle = "Proceedings of the First Workshop on Multilingual Counterspeech Generation",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.mcg-1.4/",
pages = "29--36",
abstract = "We present our approach for the shared task on Multilingual Counterspeech Generation (MCG) to counteract hate speech (HS) in Spanish, English, Basque, and Italian. To accomplish this, we followed two different strategies: 1) a graph-based generative model that encodes graph representations of knowledge related to hate speech, and 2) leveraging prompts for a large language model (LLM), specifically GPT-4o. We find that our graph-based approach tends to perform better in terms of traditional evaluation metrics (i.e., RougeL, BLEU, BERTScore), while the JudgeLM evaluation employed in the shared task favors the counter-narratives generated by the LLM-based approach, which was ranked second for English and third for Spanish on the leaderboard."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="marquez-etal-2025-nlp">
<titleInfo>
<title>NLP@IIMAS-CLTL at Multilingual Counterspeech Generation: Combating Hate Speech Using Contextualized Knowledge Graph Representations and LLMs</title>
</titleInfo>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="given">Salvador</namePart>
<namePart type="family">Márquez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Helena</namePart>
<namePart type="given">Montserrat</namePart>
<namePart type="family">Gómez Adorno</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ilia</namePart>
<namePart type="family">Markov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Selene</namePart>
<namePart type="family">Báez Santamaría</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Multilingual Counterspeech Generation</title>
</titleInfo>
<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">María</namePart>
<namePart type="given">Estrella</namePart>
<namePart type="family">Vallecillo-Rodríguez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Irune</namePart>
<namePart type="family">Zubiaga</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Arturo</namePart>
<namePart type="family">Montejo-Ráez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aitor</namePart>
<namePart type="family">Soroa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">María</namePart>
<namePart type="given">Teresa</namePart>
<namePart type="family">Martín-Valdivia</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>
<name type="personal">
<namePart type="given">Rodrigo</namePart>
<namePart type="family">Agerri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We present our approach for the shared task on Multilingual Counterspeech Generation (MCG) to counteract hate speech (HS) in Spanish, English, Basque, and Italian. To accomplish this, we followed two different strategies: 1) a graph-based generative model that encodes graph representations of knowledge related to hate speech, and 2) leveraging prompts for a large language model (LLM), specifically GPT-4o. We find that our graph-based approach tends to perform better in terms of traditional evaluation metrics (i.e., RougeL, BLEU, BERTScore), while the JudgeLM evaluation employed in the shared task favors the counter-narratives generated by the LLM-based approach, which was ranked second for English and third for Spanish on the leaderboard.</abstract>
<identifier type="citekey">marquez-etal-2025-nlp</identifier>
<location>
<url>https://aclanthology.org/2025.mcg-1.4/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>29</start>
<end>36</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NLP@IIMAS-CLTL at Multilingual Counterspeech Generation: Combating Hate Speech Using Contextualized Knowledge Graph Representations and LLMs
%A Márquez, David Salvador
%A Gómez Adorno, Helena Montserrat
%A Markov, Ilia
%A Báez Santamaría, Selene
%Y Bonaldi, Helena
%Y Vallecillo-Rodríguez, María Estrella
%Y Zubiaga, Irune
%Y Montejo-Ráez, Arturo
%Y Soroa, Aitor
%Y Martín-Valdivia, María Teresa
%Y Guerini, Marco
%Y Agerri, Rodrigo
%S Proceedings of the First Workshop on Multilingual Counterspeech Generation
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F marquez-etal-2025-nlp
%X We present our approach for the shared task on Multilingual Counterspeech Generation (MCG) to counteract hate speech (HS) in Spanish, English, Basque, and Italian. To accomplish this, we followed two different strategies: 1) a graph-based generative model that encodes graph representations of knowledge related to hate speech, and 2) leveraging prompts for a large language model (LLM), specifically GPT-4o. We find that our graph-based approach tends to perform better in terms of traditional evaluation metrics (i.e., RougeL, BLEU, BERTScore), while the JudgeLM evaluation employed in the shared task favors the counter-narratives generated by the LLM-based approach, which was ranked second for English and third for Spanish on the leaderboard.
%U https://aclanthology.org/2025.mcg-1.4/
%P 29-36
Markdown (Informal)
[NLP@IIMAS-CLTL at Multilingual Counterspeech Generation: Combating Hate Speech Using Contextualized Knowledge Graph Representations and LLMs](https://aclanthology.org/2025.mcg-1.4/) (Márquez et al., MCG 2025)
ACL