@inproceedings{kumar-etal-2026-indilangtech,
    title = "{I}ndi{L}ang{T}ech@{D}ravidian{L}ang{T}ech 2026: Hierarchical Modeling for Multi-Level Political Meme Classification",
    author = "Kumar, Saurabh  and
      G, Vivekananda  and
      Sanasam, Ranbir Singh  and
      Nandi, Sukumar",
    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-1.41/",
    pages = "273--278",
    ISBN = "979-8-89176-401-9",
    abstract = "Political memes are a widely used form of digital political expression in linguistically diverse regions such as South India, where visual cues, textual overlays, and cultural symbolism convey complex political narratives. The Shared Task on Multi-Level Political Meme Classification at DravidianLangTech 2026 introduces a hierarchical setting requiring stance identification (Support vs. Troll) and target-type prediction (Individual vs. Party) for Tamil and Malayalam memes. We propose a two-stage hierarchical framework based on the Gemma 3 4B Instruction model. Instead of jointly predicting both levels, two specialized models are fine-tuned: the first predicts meme stance, and its output conditions the second model for target identification, explicitly modeling the dependency between the meme content, the predicted stance, and the target type. Using LoRA-based parameter-efficient instruction tuning, our approach achieves an average F1-scores of 0.8029 for Tamil and 0.6950 for Malayalam across both levels, ranking 1st in Tamil and 4th in Malayalam."
}