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    <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.</abstract>
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%0 Conference Proceedings
%T FJWU_Squad at SemEval-2025 Task 1: An Idiom Visual Understanding Dataset for Idiom Learning
%A Khatoon, Maira
%A Kiyani, Arooj
%A Farid, Tehmina
%A Abdul Rauf, Sadaf
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F khatoon-etal-2025-fjwu
%X 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.
%U https://aclanthology.org/2025.semeval-1.231/
%P 1759-1765

Markdown (Informal)

[FJWU_Squad at SemEval-2025 Task 1: An Idiom Visual Understanding Dataset for Idiom Learning](https://aclanthology.org/2025.semeval-1.231/) (Khatoon et al., SemEval 2025)

• FJWU_Squad at SemEval-2025 Task 1: An Idiom Visual Understanding Dataset for Idiom Learning (Khatoon et al., SemEval 2025)

ACL

• Maira Khatoon, Arooj Kiyani, Tehmina Farid, and Sadaf Abdul Rauf. 2025. FJWU_Squad at SemEval-2025 Task 1: An Idiom Visual Understanding Dataset for Idiom Learning. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1759–1765, Vienna, Austria. Association for Computational Linguistics.