@inproceedings{sinha-etal-2026-sigjbs,
    title = "{S}ig{JBS}@{LT}-{EDI} 2026: Multimodal Homophobia and Transphobia Meme Classification",
    author = "Sinha, Gaurangi  and
      Palacharla, Rajarajeswari  and
      Jagadeeshan, Manoj Balaji",
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.28/",
    pages = "226--233",
    ISBN = "979-8-89176-424-8",
    abstract = "This paper presents our system for the LT-EDI@ACL 2026 workshop on meme classification of homophobia and transphobia in English, Hindi, and Chinese. Detecting harmful content in memes is challenging because meaning often emerges from the interaction between visual elements and short textual cues, particularly in multilingual settings. To address this, we build a multimodal pipeline using CLIP ViT-L/14 visual embeddings, EasyOCR text extraction, TF{--}IDF lexical features, and a multinomial logistic regression classifier. We further incorporate two optional expert modules, a LoRA-adapted Qwen2-VL model and a CLIP zero-shot classifier, and combine predictions using weighted majority voting. The system is intentionally lightweight and reproducible, demonstrating that strong pretrained transfer features paired with explicit OCR can provide robust multilingual meme moderation without extensive fine-tuning. On the official leaderboard, our submission ranks 1st in Hindi, 3rd in English, and 5th in Chinese."
}