@inproceedings{uddin-etal-2026-semantica,
    title = "Semantica@{D}ravidian{L}ang{T}ech 2026: Vision-Language Models for Hierarchical Political Meme Classification in {T}amil and {M}alayalam",
    author = "Uddin, Junain  and
      Datta, Rahul  and
      Abdullah, Taha Ibne  and
      Murad, Hasan",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.54/",
    pages = "348--353",
    ISBN = "979-8-89176-401-9",
    abstract = "Political memes are widely used to express opinions, sarcasm, and ideological narratives on social media platforms. However, detecting political trolling in low-resource languages such as Tamil and Malayalam remains challenging due to limited datasets and tools. To address this problem, DravidianLangTech@ACL 2026 organized a shared task on hierarchical political meme classification.This work explores text-only models, classical multimodal fusion, and Vision-Language Models (VLMs) for Tamil and Malayalam political meme classification. Our experiments include IndicBERTv2, XLM-RoBERTa, EfficientNet-based multimodal fusion, and Qwen-VL models. Among the submitted systems, Qwen2.5-VL-7B-Instruct with 4-bit QLoRA fine-tuning achieved competitive performance, ranking 3rd in the Malayalam track and 4th in the Tamil track based on weighted-F1 score. Additional post-evaluation experiments with Qwen3-VL-8B further improved macro-F1 performance, highlighting the effectiveness of VLMs for low-resource multilingual political meme classification."
}