@inproceedings{prasad-etal-2022-gjg,
    title = "{GJG}@{T}amil{NLP}-{ACL}2022: Emotion Analysis and Classification in {T}amil using Transformers",
    author = "Prasad, Janvi  and
      Prasad, Gaurang  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-1.14/",
    doi = "10.18653/v1/2022.dravidianlangtech-1.14",
    pages = "86--92",
    abstract = "This paper describes the systems built by our team for the ``Emotion Analysis in Tamil'' shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. There were two multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A contained 11 types of emotions while sub-task B was more fine-grained with 31 emotions. We fine-tuned an XLM-RoBERTa and DeBERTA base model for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.46 and the DeBERTa model achieved an accuracy of 0.45. We had the best classification performance out of 11 teams for sub-task A. For sub-task B, the XLM-RoBERTa model{'}s accuracy was 0.33 and the DeBERTa model had an accuracy of 0.26. We ranked 2nd out of 7 teams for sub-task B."
}