@inproceedings{dhasan-2025-necto,
    title = "Necto@{D}ravidian{L}ang{T}ech 2025: Fine-tuning Multilingual {M}ini{LM} for Text Classification in {D}ravidian Languages",
    author = "Dhasan, Livin Nector",
    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/landing_page/2025.dravidianlangtech-1.56/",
    pages = "320--324",
    ISBN = "979-8-89176-228-2",
    abstract = "This paper explores the application of a fine-tuned Multilingual MiniLM model for various binary text classification tasks, including AI-generated product review detection, abusive language targeting woman detection, and fake news detection in the Dravidian languages Tamil and Malayalam. This work was done as part of submissions to shared tasks organized by DravidianLangTech@NAACL 2025. The model was fine-tuned using both Tamil and Malayalam datasets, and its performance was evaluated across different tasks using macro F1-score. The results indicate that this model produces performance that is very close to the best F1 score reported by other teams. An investigation is conducted on the AI-generated product review dataset and the findings are reported."
}