@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}