@inproceedings{r-l-m-2023-nitk,
    title = "{NITK}-{IT}-{NLP}@{D}ravidian{L}ang{T}ech: Impact of Focal Loss on {M}alayalam Fake News Detection using Transformers",
    author = "R L, Hariharan  and
      M, Anand Kumar",
    editor = "Chakravarthi, Bharathi R.  and
      Priyadharshini, Ruba  and
      M, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.dravidianlangtech-1.29/",
    pages = "207--210",
    abstract = "Fake News Detection in Dravidian Languages is a shared task that identifies youtube comments in the Malayalam language for fake news detection. In this work, we have proposed a transformer-based model with cross-entropy loss and focal loss, which classifies the comments into fake or authentic news. We have used different transformer-based models for the dataset with modifications in the experimental setup, out of which the fine-tuned model, which is based on MuRIL with focal loss, achieved the best overall macro F1-score of 0.87, and we got second position in the final leaderboard."
}