@inproceedings{biradar-saumya-2022-iiitdwd,
    title = "{IIITDWD}@{T}amil{NLP}-{ACL}2022: Transformer-based approach to classify abusive content in {D}ravidian Code-mixed text",
    author = "Biradar, Shankar  and
      Saumya, Sunil",
    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/fix-sig-urls/2022.dravidianlangtech-1.16/",
    doi = "10.18653/v1/2022.dravidianlangtech-1.16",
    pages = "100--104",
    abstract = "Identifying abusive content or hate speech in social media text has raised the research community{'}s interest in recent times. The major driving force behind this is the widespread use of social media websites. Further, it also leads to identifying abusive content in low-resource regional languages, which is an important research problem in computational linguistics. As part of ACL-2022, organizers of DravidianLangTech@ACL 2022 have released a shared task on abusive category identification in Tamil and Tamil-English code-mixed text to encourage further research on offensive content identification in low-resource Indic languages. This paper presents the working notes for the model submitted by IIITDWD at DravidianLangTech@ACL 2022. Our team competed in Sub-Task B and finished in 9th place among the participating teams. In our proposed approach, we used a pre-trained transformer model such as Indic-bert for feature extraction, and on top of that, SVM classifier is used for stance detection. Further, our model achieved 62 {\%} accuracy on code-mixed Tamil-English text."
}