@inproceedings{karunanidhi-arumugam-2026-chmod,
    title = "{CHMOD}{\_}777@{D}ravidian{L}ang{T}ech 2026: Context-Aware Fine-tuned {M}u{RIL} for Abusive {T}amil Text Detection on Social Media",
    author = "Karunanidhi, Arunaggiri Pandian  and
      Arumugam, Prabalakshmi",
    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-1.22/",
    pages = "176--180",
    ISBN = "979-8-89176-401-9",
    abstract = "This paper describes Team CHMOD{\_}777{'}s system for the DravidianLangTech@ACL 2026 shared task on detecting abusive Tamil text targeting women on social media. We fine-tune three transformer backbones (MuRIL, XLM-RoBERTa, IndicBERT-v3) with Focal Loss and weighted sampling, systematically evaluating the effects of context length, hyperparameter tuning, and language-specific pre-training. Our best system, MuRIL with 256-token context, achieves 82.76{\%} Macro F1 on the development set and 80.61{\%} on the official test set, ranking 6th out of 24 teams. We find that (1) extending context from 128 to 256 tokens improves F1 while converging 2.4x faster, (2) language-specific pre-training (MuRIL, 236M) outperforms larger models (IndicBERT, 270M), and (3) default hyperparameters are optimal, with every tuning attempt degrading performance."
}