@inproceedings{s-b-priya-b-2026-rspectnlp,
    title = "{R}spect{NLP}@{LT}-{EDI} 2026:Rubric-Driven Prompting for Safe Multilingual Counter Narrative Generation",
    author = "S.b.priya  and
      B, Bharathi",
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.25/",
    pages = "212--216",
    ISBN = "979-8-89176-424-8",
    abstract = "The problem of harmful online discourse against the LGBTQ+ community is still a concern on social media platforms. Although hate speech detection is a well-explored area, the task of constructive counter-narrative generation is still an emerging field of research, especially in the multilingual and low-resource settings. Counter-narratives are designed to counter harmful discourse with respectful and empathetic responses, as opposed to mere content deletion. In this paper, the model proposes a zero-shot multilingual system for counter-narrative generation in English and Tamil. The proposed system employs the pretrained google/flan-t5-base transformer model guided by rubric-aligned prompts to encourage politeness, contextual relevance, and non-toxic response generation. The system operates in a zero-shot setting without task-specific fine-tuning and uses beam search decoding for controlled response generation. On the English test data, the system scored an overall score of 70.33 per cent with a contextual coherence score of 81.82 per cent. On the Tamil test data, the system scored an overall score of 33.57 per cent with significantly lower scores on coherence and quality. These findings indicate that structured prompting can facilitate safe and coherent generation in English, but also underscore the challenges of zero-shot multilingual models in low-resource language scenarios."
}