@inproceedings{prakash-etal-2026-dpr,
    title = "{DPR}@{D}ravidian{L}ang{T}ech 2026: Transformer-Based Abusive Content Detection for {T}amil Text Targeting Women on Social Media",
    author = "Prakash, Diya  and
      S, Praveen Kumar  and
      Kumar, R Ranjith  and
      Palani, Balasubramanian  and
      Jose, Jobin  and
      Rajamanickam, Siranjeevi",
    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-1.35/",
    pages = "242--247",
    ISBN = "979-8-89176-401-9",
    abstract = "The fast-growing number of content in Tamil in social media has led to increasing abusive and gender-directed hate speech in online platforms. Detecting abusive content written in Tamil is relatively difficult owing to the complex morphological structure of Tamil language, its dialects, transliteration, and contextualized usage. In this study, the use of transformer-based pretrained language models in detecting abusive content in Tamil was explored. Five transformer-based models{---}mBERT, MuRIL, XLM-RoBERTa, IndicBERT, and Tamil-BERT{---}were fine-tuned and tested using DravidianLangTech 2026 shared task dataset. The experimental results show that the best-performing model was Tamil-BERT with an accuracy rate of 80.72{\%} owing to Tamil-specific pretraining and better morphological analysis capabilities. Our system ranks 5th at the leaderboard of the DravidianLangTech 2026 shared task challenge. The source code and fine-tuned models are opensource and publicly accessible."
}