@inproceedings{r-rajalakshmi-2026-dlrg,
    title = "{DLRG}@{LT}-{EDI} 2026: Automating Counter-Narratives for Homophobic and Transphobic Comments",
    author = "R, Ramesh Kannan  and
      Rajalakshmi, Ratnavel",
    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-1.16/",
    pages = "161--166",
    ISBN = "979-8-89176-424-8",
    abstract = "Online hate speech is spreading rapidly, creating significant challenge, particularly in low-resource language such as Tamil. Lack of developed automated content moderation systems makes it difficult to control harmful content effectively. In this study, we propose a computational framework for generating Counter Narratives (CNs) using classical NLP techniques. With this, we leverage TF-IDF features with n-grams to identify the labels as Homophobic or Transphobic. Span detection is performed with TF-IDF features with n-grams and Machine learning models. Counter narratives are then retrieved by computing cosine similarity, ensuring semantic alignment and contextual relevance. Evaluation on the expanded human curated dataset demonstrates that our approach produces contextually appropriate and semantically coherent counter narratives. Notably, the proposed system is submitted at Task 2 shown a overall average score of 80.40 {\%} for Tamil and 77.29 {\%} for English and secured first and fourth rank respectively. GitHub: https://github.com/kannanrrk/Span-Counter-Feature-Based"
}