@inproceedings{roy-etal-2025-lexilogic,
    title = "{L}exi{L}ogic@{D}ravidian{L}ang{T}ech 2025: Multimodal Hate Speech Detection in {D}ravidian languages",
    author = "Roy, Billodal  and
      Gupta, Pranav  and
      Bhattacharyya, Souvik  and
      M, Niranjan Kumar",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.dravidianlangtech-1.95/",
    pages = "552--556",
    ISBN = "979-8-89176-228-2",
    abstract = "This paper describes our participation in the DravidianLangTech@NAACL 2025 shared task on hate speech detection in Dravidian languages. While the task provided both text transcripts and audio data, we demonstrate that competitive results can be achieved using text features alone. We employed fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models from l3cube-pune for Malayalam, Tamil, and Telugu languages. Our system achieved notable results, securing second position for Tamil and Malayalam tasks, and first position for Telugu in the official leaderboard."
}