@article{chang-etal-2026-beyond,
    title = "Beyond Generic Responses: Target-Aware Strategies for Countering Hate Speech",
    author = "Chang, Yen-Yu  and
      Dementieva, Daryna  and
      Fraser, Alexander",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.1/",
    pages = "1--18",
    abstract = "Effective counter-narratives (CNs) are essential for combating online hate speech, yet generic responses often fail to address the specific needs of targeted groups. This paper proposes a target-aware CN generation framework that incorporates demographic-specific tokens into transformer-based models. Our approach enhances the contextual relevance by introducing target-group tokens into the model{'}s vocabulary. To assess CN quality, we employ a multifaceted evaluation framework, including automatic metrics and LLM as Judges (JudgeLM). Evaluation with a wide range of language models demonstrates that target group tokens markedly improve contextual relevance of generated CN, particularly in small and medium models, with measurable gains in validity as CN and contextual relevance. Even for large instruction-tuned models, such as LLaMA-3, incorporating target-specific information proves effective in enhancing contextual relevance of generated responses. Warning: This paper contains offensive texts that are only used for combating online hate."
}