@inproceedings{prasad-etal-2022-gjg-tamilnlp,
    title = "{GJG}@{T}amil{NLP}-{ACL}2022: Using Transformers for Abusive Comment Classification in {T}amil",
    author = "Prasad, Gaurang  and
      Prasad, Janvi  and
      C, Gunavathi",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Krishnamurthy, Parameswari  and
      Sherly, Elizabeth  and
      Mahesan, Sinnathamby",
    booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.dravidianlangtech-1.15/",
    doi = "10.18653/v1/2022.dravidianlangtech-1.15",
    pages = "93--99",
    abstract = "This paper presents transformer-based models for the ``Abusive Comment Detection'' shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. Our team participated in both the multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A was in Tamil text; while B was code-mixed Tamil-English text. Both the datasets contained 8 classes of abusive comments. We trained an XLM-RoBERTa and DeBERTA base model on the training splits for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.66 and the DeBERTa model achieved an accuracy of 0.62. For sub-task B, both the models achieved a classification accuracy of 0.72; however, the DeBERTa model performed better in other classification metrics. Our team ranked 2nd in the code-mixed classification sub-task and 8th in Tamil-text sub-task."
}