@inproceedings{manukonda-etal-2025-bytesizedllm,
    title = "byte{S}ized{LLM}@{D}ravidian{L}ang{T}ech 2025: Multimodal Hate Speech Detection in {M}alayalam Using Attention-Driven {B}i{LSTM}, {M}alayalam-Topic-{BERT}, and Fine-Tuned {W}av2{V}ec 2.0",
    author = "Manukonda, Durga Prasad  and
      Kodali, Rohith Gowtham  and
      Iglesias, Daniel",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.dravidianlangtech-1.12/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.12",
    pages = "68--73",
    ISBN = "979-8-89176-228-2",
    abstract = "This research presents a robust multimodal framework for hate speech detection in Malayalam, combining fine-tuned Wav2Vec 2.0, Malayalam-Doc-Topic-BERT, and an Attention-Driven BiLSTM architecture. The proposed approach effectively integrates acoustic and textual features, achieving a macro F1-score of 0.84 on the Malayalam test set. Fine-tuning Wav2Vec 2.0 on Malayalam speech data and leveraging Malayalam-Doc-Topic-BERT significantly improved performance over prior methods using openly available models. The results highlight the potential of language-specific models and advanced multimodal fusion techniques for addressing nuanced hate speech categories, setting the stage for future work on Dravidian languages like Tamil and Telugu."
}