@inproceedings{hossain-etal-2025-semanticcuetsync,
    title = "{S}emantic{C}uet{S}ync@{D}ravidian{L}ang{T}ech 2025: Multimodal Fusion for Hate Speech Detection - A Transformer Based Approach with Cross-Modal Attention",
    author = "Hossain, Md. Sajjad  and
      Shohan, Symom Hossain  and
      Paran, Ashraful Islam  and
      Hossain, Jawad  and
      Hoque, Mohammed Moshiul",
    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/Ingest-2025-COMPUTEL/2025.dravidianlangtech-1.86/",
    pages = "489--495",
    ISBN = "979-8-89176-228-2",
    abstract = "The rise of social media has significantly facilitated the rapid spread of hate speech. Detecting hate speech for content moderation is challenging, especially in low-resource languages (LRLs) like Telugu. Although some progress has been noticed in hate speech detection in Telegu concerning unimodal (text or image) in recent years, there is a lack of research on hate speech detection based on multimodal content detection (specifically using audio and text). In this regard, DravidianLangTech has arranged a shared task to address this challenge. This work explored three machine learning (ML), three deep learning (DL), and seven transformer-based models that integrate text and audio modalities using cross-modal attention for hate speech detection. The evaluation results demonstrate that mBERT achieved the highest F-1 score of 49.68{\%} using text. However, the proposed multimodal attention-based approach with Whisper-small+TeluguBERT-3 achieved an F-1 score of 43 68{\%}, which helped us achieve a rank of 3rd in the shared task competition."
}