@inproceedings{v-2025-rssn,
title = "{RSSN} at Multilingual Counterspeech Generation: Leveraging Lightweight Transformers for Efficient and Context-Aware Counter-Narrative Generation",
author = "V, Ravindran",
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-1.2/",
pages = "13--18",
abstract = "This paper presents a system for counter-speech generation, developed for the COLING 2025 shared task. By leveraging lightweight transformer models, DistilBART and T5-small, we optimize computational efficiency while maintaining strong performance. The work includes an in-depth analysis of a multilingual dataset, addressing hate speech instances across diverse languages and target groups. Through systematic error analysis, we identify challenges such as lack of specificity and context misinterpretation in generated counter-narratives. Evaluation metrics like BLEU, ROUGE, and BERTScore demonstrate the effectiveness of our approaches, while comparative insights highlight complementary strengths in fluency, contextual integration, and creativity. Future directions focus on enhancing preprocessing, integrating external knowledge sources, and improving scalability."
}
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<abstract>This paper presents a system for counter-speech generation, developed for the COLING 2025 shared task. By leveraging lightweight transformer models, DistilBART and T5-small, we optimize computational efficiency while maintaining strong performance. The work includes an in-depth analysis of a multilingual dataset, addressing hate speech instances across diverse languages and target groups. Through systematic error analysis, we identify challenges such as lack of specificity and context misinterpretation in generated counter-narratives. Evaluation metrics like BLEU, ROUGE, and BERTScore demonstrate the effectiveness of our approaches, while comparative insights highlight complementary strengths in fluency, contextual integration, and creativity. Future directions focus on enhancing preprocessing, integrating external knowledge sources, and improving scalability.</abstract>
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%0 Conference Proceedings
%T RSSN at Multilingual Counterspeech Generation: Leveraging Lightweight Transformers for Efficient and Context-Aware Counter-Narrative Generation
%A V, Ravindran
%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 v-2025-rssn
%X This paper presents a system for counter-speech generation, developed for the COLING 2025 shared task. By leveraging lightweight transformer models, DistilBART and T5-small, we optimize computational efficiency while maintaining strong performance. The work includes an in-depth analysis of a multilingual dataset, addressing hate speech instances across diverse languages and target groups. Through systematic error analysis, we identify challenges such as lack of specificity and context misinterpretation in generated counter-narratives. Evaluation metrics like BLEU, ROUGE, and BERTScore demonstrate the effectiveness of our approaches, while comparative insights highlight complementary strengths in fluency, contextual integration, and creativity. Future directions focus on enhancing preprocessing, integrating external knowledge sources, and improving scalability.
%U https://aclanthology.org/2025.mcg-1.2/
%P 13-18
Markdown (Informal)
[RSSN at Multilingual Counterspeech Generation: Leveraging Lightweight Transformers for Efficient and Context-Aware Counter-Narrative Generation](https://aclanthology.org/2025.mcg-1.2/) (V, MCG 2025)
ACL