| Title: |
TrenTeam at Multilingual Counterspeech Generation: Multilingual Passage Re-Ranking Approaches for Knowledge-Driven Counterspeech Generation Against Hate |
| Authors: |
Russo D. |
| Contributors: |
Helena Bonaldi, María Estrella Vallecillo-Rodríguez, Irune Zubiaga, Arturo Montejo-Ráez, Aitor Soroa, María Teresa Martín-Valdivia, Marco Guerini, Rodrigo Agerri; Russo, D. |
| Publisher Information: |
Association for Computational Linguistics |
| Publication Year: |
2025 |
| Collection: |
Fondazione Bruno Kessler: CINECA IRIS |
| Description: |
Hate speech (HS) in online spaces poses severe risks, including real-world violence and psychological harm to victims, necessitating effective countermeasures. Counterspeech (CS), which responds to hateful messages with opposing yet non-hostile narratives, offer a promising solution by mitigating HS while upholding free expression. However, the growing volume of HS demands automation, making Natural Language Processing a viable solution for the automatic generation of CS. Recent works have explored knowledge-driven approaches, leveraging external sources to improve the relevance and informativeness of responses. These methods typically involve multi-step pipelines combining retrieval and passage re-ranking modules. While effective, most studies have focused on English, with limited exploration of multilingual contexts. This paper addresses these gaps by proposing a multilingual, knowledge-driven approach to CS generation. We integrate state-of-the-art re-ranking mechanisms into the CS generation pipeline and evaluate them using the MT-CONAN-KN dataset, which includes hate speech, relevant knowledge sentences, and counterspeech in four languages: English, Italian, Spanish, and Basque. Our approach compares reranker-based systems employing multilingual cross-encoders and LLMs to a simpler end-to-end system where the language model directly handles both knowledge selection and CS generation. Results demonstrate that reranker-based systems outperformed end-to-end systems in syntactic and semantic similarity metrics, with LLM-based re-rankers delivering the strongest performance overall. This work is the result of our participation in the Shared Task on Multilingual Counterspeech Generation held at COLING 2025. |
| Document Type: |
conference object |
| File Description: |
ELETTRONICO |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/isbn/9798891762077; ispartofbook:Proceedings of the First Workshop on Multilingual Counterspeech Generation; The 31st International Conference on Computational Linguistics (COLING 2025); firstpage:77; lastpage:91; numberofpages:15; serie:INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS; https://hdl.handle.net/11582/369647 |
| Availability: |
https://hdl.handle.net/11582/369647; https://aclanthology.org/2025.mcg-1.9/ |
| Accession Number: |
edsbas.622F5AD6 |
| Database: |
BASE |