@inproceedings{khatoon-etal-2025-fjwu,
    title = "{FJWU}{\_}{S}quad at {S}em{E}val-2025 Task 1: An Idiom Visual Understanding Dataset for Idiom Learning",
    author = "Khatoon, Maira  and
      Kiyani, Arooj  and
      Farid, Tehmina  and
      Abdul Rauf, Sadaf",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.231/",
    pages = "1759--1765",
    ISBN = "979-8-89176-273-2",
    abstract = "Idiomatic expressions pose difficulties for Natural Language Processing (NLP) because they are noncompositional. In this paper, we propose the Idiom Visual Understanding Dataset (IVUD), a multimodal dataset for idiom understanding using visual and textual representation. For SemEval-2025 Task 1 (AdMIRe), we specifically addressed dataset augmentation using AI-synthesized images and human-directed prompt engineering. We compared the efficacy of vision- and text-based models in ranking images aligned with idiomatic phrases. The results identify the advantages of using multimodal context for enhanced idiom understanding, showcasing how vision-language models perform better than text-only approaches in the detection of idiomaticity."
}