@inproceedings{pan-etal-2025-umuteam,
    title = "{UMUT}eam at {S}em{E}val-2025 Task 1: Leveraging Multimodal and Large Language Model for Identifying and Ranking Idiomatic Expressions",
    author = "Pan, Ronghao  and
      Bernal - Beltr{\'a}n, Tom{\'a}s  and
      Garc{\'i}a - D{\'i}az, Jos{\'e} Antonio  and
      Valencia - Garc{\'i}a, Rafael",
    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-1.101/",
    pages = "743--749",
    ISBN = "979-8-89176-273-2",
    abstract = "Idioms are non-compositional linguistic expressions whose meanings cannot be directly inferred from the individual words that compose them, posing significant challenges for natural language processing systems. This paper describes the participation of the UMUTeam in Subtask A of the AdMIRe shared task (SemEval 2025), which focuses on understanding idiomatic expressions through visual and contextual representations in English and Portuguese. Specifically, the task involves ranking a set of images according to how well they represent the sense of a potentially idiomatic nominal compound within a given contextual sentence. To address this challenge, we adopted a multimodal approach that combines textual and visual features using pre-trained language models, such as BERT and XLM-RoBERTa, along with Vision Transformers. Additionally, we explored the in-context learning capabilities of Large Language Models (LLMs), particularly Llama-3.1-8B, for image classification. These models are trained using a regression approach to rank images according to their semantic alignment with the contextual meaning of idioms. The results show that the Llama-3.1-8B model performs best for English, ranking 32 out of 36, while the XLM + ViT model is more effective for Portuguese, ranking 21 out of 24."
}