@inproceedings{murali-sivanaiah-2025-ssn,
    title = "{SSN}{\_}{MMHS}@{D}ravidian{L}ang{T}ech 2025: A Dual Transformer Approach for Multimodal Hate Speech Detection in {D}ravidian Languages",
    author = "Murali, Jahnavi  and
      Sivanaiah, Rajalakshmi",
    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/fix-sig-urls/2025.dravidianlangtech-1.36/",
    pages = "210--214",
    ISBN = "979-8-89176-228-2",
    abstract = "The proliferation of the Internet and social media platforms has resulted in an alarming increase in online hate speech, negatively affecting individuals and communities worldwide. While most research focuses on text-based detection in English, there is an increasing demand for multilingual and multimodal approaches to address hate speech more effectively. This paper presents a methodology for multiclass hate speech classification in low-resource Indian languages namely, Malayalam, Telugu, and Tamil, as part of the shared task at DravidianLangTech 2025. Our proposed approach employs a dual transformer-based framework that integrates audio and text modalities, facilitating cross-modal learning to enhance detection capabilities. Our model achieved macro-F1 scores of 0.348, 0.1631, and 0.1271 in the Malayalam, Telugu, and Tamil subtasks respectively. Although the framework{'}s performance is modest, it provides valuable insights into the complexities of multimodal hate speech detection in low-resource settings and highlights areas for future improvement, including data augmentation, and alternate fusion and feature extraction techniques."
}