@inproceedings{rebayet-etal-2026-trivector,
    title = "{T}ri{V}ector@{D}ravidian{L}ang{T}ech 2026: Abusive {T}amil Text Detection on Social Media Using Lexicon-Augmented Transformers",
    author = "Rebayet, Oarisa  and
      Eid, Tahmima Hoque  and
      Tabassum, Fawzia  and
      Murad, Hasan",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.67/",
    pages = "420--428",
    ISBN = "979-8-89176-401-9",
    abstract = "Abusive comment detection in low-resource languages poses significant challenges, particularly when targeting gender-based abuse on social media platforms. This work presents our system for `Abusive Tamil text targeting women on social media' at DravidianLangTech@ACL 2026. We introduce nine handcrafted lexicon features integrated with pretrained multilingual transformer embeddings and evaluate their effectiveness in classifying Tamil online comments as abusive or non-abusive. To better understand their impact, we compare model performance with and without these lexical attributes across multiple transformer architectures. Our best-performing model, XLM-RoBERTa-Large, achieved a macro F1-score of 81.71{\%}, securing 15th rank in the competition. The findings indicate that larger multilingual models generalize more effectively to unseen data compared to smaller domain-specific models, while the addition of lexical features yields only mild gains."
}