@inproceedings{iyer-etal-2026-team,
    title = "{T}eam{\_}{O}ne@{D}ravidian{L}ang{T}ech 2026: A Gated Multimodal Architecture for Multi-Level Stance and Target Detection in {M}alayalam Political Memes",
    author = "Iyer, Nimisha M  and
      N, Ashmi S  and
      Palani, Balasubramanian  and
      Jose, Jobin  and
      Rajamanickam, Siranjeevi",
    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-1.65/",
    pages = "409--413",
    ISBN = "979-8-89176-401-9",
    abstract = "Stance and target detection in multimodal political memes presents notable challenges in low-resource and highly imbalanced settings.This task is based on the Malayalam dataset from the DravidianLangTech 2026 Shared Task(500 samples with a 95.4:4.6 stance imbalance).The primary challenges stem from linguistic variability and visually complex meme formats,which hinder accurate text extraction and effective multimodal alignment. A lightweight yet high-performing multimodal framework is proposed that integrates bilingual OCR, a Vision Transformer (ViT), and IndicBERT to learn complementary visual and textual representations. A gated fusion mechanism effectivelycombines multimodal features, while asymmetric loss weighting and post-training threshold optimization address extreme class imbalance. The methodology achieves a Weighted F1-score of 0.9535 for stance detection and 0.5283 for target identification, demonstrating strong robustness and generalization under realistic multimodal constraints."
}