@inproceedings{jaishri-etal-2026-susmitha,
    title = "Susmitha@{LT}-{EDI} 2026: Detecting {LGBTQ}+ Phobia in Multilingual Memes via Joint Representation",
    author = "Jaishri, Susmitha  and
      Shanmugavadivel, Kogilavani  and
      Subramanian, Malliga  and
      R, Mouleeshuwarapprabu",
    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-1.27/",
    pages = "222--225",
    ISBN = "979-8-89176-424-8",
    abstract = "The automated detection of LGBTQ+ phobia in social media memes is essential for fostering inclusive digital environments, yet it remains challenging due to the complex interplay of visual metaphors and multilingual text. We participated in the ``Homophobia and Transphobia Meme Classification'' shared task at LT-EDI 2026, evaluating a multimodal architecture across English, Hindi, and Chinese tracks. Our system employs a late-fusion strategy: XLM-RoBERTa encodes OCR-extracted text into a representation $h_{t} \in \mathbb{R}^{768}$ , while CLIP extracts visual features $h_{v} \in \mathbb{R}^{512}$. These are concatenated into a joint vector $z = [h_{t} \oplus h_{v}] \in \mathbb{R}^{1280}$ and processed via a non-linear multilayer perceptron to capture cross-modal interactions.The system demonstrated robust performance in high-resource contexts, securing 3rd rank in the Chinese track (Macro F1: 0.7371) and 4th rank in the English track (Macro F1: 0.6121). In contrast, the Hindi track results (Macro F1: 0.1616) revealed significant challenges related to script complexity and class imbalance. These findings underscore the effectiveness of global transformer-based models for multimodal reasoning while highlighting the ongoing need for specialized linguistic refinement in low-resource and diverse script environments"
}