@inproceedings{a-kumar-2026-nitc,
    title = "{NITC}-{HSR}@{D}ravidian{L}ang{T}ech 2026: Ensembling Multilingual Transformer Models for Detecting Abusive {T}amil Text Targeting Women on Social Media",
    author = "A, Rameez Mohammed  and
      Kumar, S D Madhu",
    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-1.48/",
    pages = "316--320",
    ISBN = "979-8-89176-401-9",
    abstract = "The proliferation of misogynistic content on social media platforms is a serious problem that requires the development of automated detection systems, which is a challenging task for low-resource languages like Tamil. This study investigates the effectiveness of multilingual transformer models for identifying abusive Tamil text targeting women in social media. Results indicate that such models achieve strong baseline performance on this task. Furthermore, an ensemble of two best performing models was found to improve the classification performance further. The results also highlighted the significance of domain-specific pre-training for improving classifier performance. The best performing ensemble model achieved a weighted F1 score of 0.83 on the test set, placing our approach in first position in the shared task."
}