@inproceedings{c-etal-2025-ssn-hate,
    title = "{SSN}{\_}{IT}{\_}{HATE}@{LT}-{EDI}-2025: Caste and Migration Hate Speech Detection",
    author = "C, Maria Nancy  and
      N, Radha  and
      R, Swathika",
    editor = "Gkirtzou, Katerina  and
      {\v{Z}}itnik, Slavko  and
      Gracia, Jorge  and
      Gromann, Dagmar  and
      di Buono, Maria Pia  and
      Monti, Johanna  and
      Ionov, Maxim",
    booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = sep,
    year = "2025",
    address = "Naples, Italy",
    publisher = "Unior Press",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.ltedi-1.14/",
    pages = "84--89",
    ISBN = "978-88-6719-334-9",
    abstract = "This paper proposes a transformer-based methodology for detecting hate speech in Tamil, developed as part of the shared task on Caste and Migration Hate Speech Detection. Leveraging the multilingual BERT (mBERT) model, we fine-tune it to classify Tamil social media content into caste/migration-related hate speech and non hate speech categories. Our approach achieves a macro F1-score of 0.72462 in the development dataset, demonstrating the effectiveness of multilingual pretrained models in low-resource language settings. The code for this work is available on github Hate-Speech Deduction."
}