@inproceedings{rahman-etal-2025-cuet,
    title = "{CUET}{\_}{I}gnite@{D}ravidian{L}ang{T}ech 2025: Detection of Abusive Comments in {T}amil Text Using Transformer Models",
    author = "Rahman, MD.Mahadi  and
      Uddin, Mohammad Minhaj  and
      Arefin, Mohammad Shamsul",
    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-1.70/",
    pages = "392--397",
    ISBN = "979-8-89176-228-2",
    abstract = "Abusive comment detection in low-resource languages is a challenging task particularly when addressing gender-based abuse. Identifying abusive language targeting women is crucial for effective content moderation and fostering safer online spaces. A shared task on abusive comment detection in Tamil text organized by DravidianLangTech@NAACL 2025 allowed us to address this challenge using a curated dataset. For this task, we experimented with various machine learning (ML) and deep learning (DL) models including Logistic Regression, Random Forest, SVM, CNN, LSTM, BiLSTMand transformer-based models such as mBERT, IndicBERT, XLMRoBERTa and many more. The dataset comprised of Tamil YouTube comments annotated with binary labels, Abusive and NonAbusive capturing explicit abuse, implicit biases and stereotypes. Our experiments demonstrated that XLM-RoBERTa achieved the highest macro F1-score(0.80), highlighting its effectiveness in handling Tamil text. This research contributes to advancing abusive language detection and natural language processing in lowresource languages particularly for addressing gender-based abuse online."
}