@inproceedings{saathvik-etal-2025-jas,
    title = "{JAS}@{D}ravidian{L}ang{T}ech 2025: Abusive {T}amil Text targeting Women on Social Media",
    author = "Saathvik, B  and
      Sivakumar, Janeshvar  and
      Durairaj, Thenmozhi",
    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-1.6/",
    pages = "28--32",
    ISBN = "979-8-89176-228-2",
    abstract = "This paper presents our submission for Abusive Comment Detection in Tamil - DravidianLangTech@NAACL 2025. The aim is to classify whether a given comment is abusive towards women. Google{'}s MuRIL (Khanujaet al., 2021), a transformer-based multilingual model, is fine-tuned using the provided dataset to build the classification model. The datasetis preprocessed, tokenised, and formatted for model training. The model is trained and evaluated using accuracy, F1-score, precision, andrecall. Our approach achieved an evaluation accuracy of 77.76{\%} and an F1-score of 77.65{\%}. The lack of large, high-quality datasets forlow-resource languages has also been acknowledged."
}