@inproceedings{lyu-etal-2025-hw,
title = "{HW}-{TSC} at Multilingual Counterspeech Generation",
author = "Lyu, Xinglin and
Wang, Haolin and
Zhang, Min and
Yang, Hao",
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-1.6/",
pages = "47--55",
abstract = "Multilingual counterspeech generation (MCSG) contributes to generating counterspeech with respectful, non-offensive information that is specific and truthful for the given hate speech, especially those for languages other than English. Generally, the training data of MCSG in low-source language is rare and hard to curate. Even with the impressive large language models (LLMs), it is a struggle to generate an appreciative counterspeech under the multilingual scenario. In this paper, we design a pipeline with a generation-reranking mode to effectively generate counterspeech under the multilingual scenario via LLM. Considering the scarcity of training data, we first utilize the training-free strategy, i.e., in-context learning (ICL), to generate the candidate counterspeechs. Then, we propose to rerank those candidate counterspeech via the Elo rating algorithm and a fine-tuned reward model. Experimental results on four languages, including English (EN), Italian (IT), Basque (EU) and Spanish (ES), our system achieves a comparative or even better performance in four metrics compared to the winner in this shared task."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lyu-etal-2025-hw">
<titleInfo>
<title>HW-TSC at Multilingual Counterspeech Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xinglin</namePart>
<namePart type="family">Lyu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haolin</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hao</namePart>
<namePart type="family">Yang</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>Multilingual counterspeech generation (MCSG) contributes to generating counterspeech with respectful, non-offensive information that is specific and truthful for the given hate speech, especially those for languages other than English. Generally, the training data of MCSG in low-source language is rare and hard to curate. Even with the impressive large language models (LLMs), it is a struggle to generate an appreciative counterspeech under the multilingual scenario. In this paper, we design a pipeline with a generation-reranking mode to effectively generate counterspeech under the multilingual scenario via LLM. Considering the scarcity of training data, we first utilize the training-free strategy, i.e., in-context learning (ICL), to generate the candidate counterspeechs. Then, we propose to rerank those candidate counterspeech via the Elo rating algorithm and a fine-tuned reward model. Experimental results on four languages, including English (EN), Italian (IT), Basque (EU) and Spanish (ES), our system achieves a comparative or even better performance in four metrics compared to the winner in this shared task.</abstract>
<identifier type="citekey">lyu-etal-2025-hw</identifier>
<location>
<url>https://aclanthology.org/2025.mcg-1.6/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>47</start>
<end>55</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T HW-TSC at Multilingual Counterspeech Generation
%A Lyu, Xinglin
%A Wang, Haolin
%A Zhang, Min
%A Yang, Hao
%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 lyu-etal-2025-hw
%X Multilingual counterspeech generation (MCSG) contributes to generating counterspeech with respectful, non-offensive information that is specific and truthful for the given hate speech, especially those for languages other than English. Generally, the training data of MCSG in low-source language is rare and hard to curate. Even with the impressive large language models (LLMs), it is a struggle to generate an appreciative counterspeech under the multilingual scenario. In this paper, we design a pipeline with a generation-reranking mode to effectively generate counterspeech under the multilingual scenario via LLM. Considering the scarcity of training data, we first utilize the training-free strategy, i.e., in-context learning (ICL), to generate the candidate counterspeechs. Then, we propose to rerank those candidate counterspeech via the Elo rating algorithm and a fine-tuned reward model. Experimental results on four languages, including English (EN), Italian (IT), Basque (EU) and Spanish (ES), our system achieves a comparative or even better performance in four metrics compared to the winner in this shared task.
%U https://aclanthology.org/2025.mcg-1.6/
%P 47-55
Markdown (Informal)
[HW-TSC at Multilingual Counterspeech Generation](https://aclanthology.org/2025.mcg-1.6/) (Lyu et al., MCG 2025)
ACL
- Xinglin Lyu, Haolin Wang, Min Zhang, and Hao Yang. 2025. HW-TSC at Multilingual Counterspeech Generation. In Proceedings of the First Workshop on Multilingual Counterspeech Generation, pages 47–55, Abu Dhabi, UAE. Association for Computational Linguistics.