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SemEval-2025 Task 1: AdMIRe - Advancing Multimodal Idiomaticity Representation

Summary

Idioms remain difficult for NLP systems because their meaning usually cannot be built from the meanings of their parts. Even strong LLMs handle figurative language inconsistently. The AdMIRe shared task tests whether models can link potentially idiomatic noun compounds to images that reflect the sense intended in context, in English and Brazilian Portuguese.

The organisers selected compounds with both a plausible literal reading and a plausible figurative reading, each of which could be depicted. Native speakers wrote scene descriptions, which were then rendered as cartoon-style images with Midjourney. LLaVA produced captions for the images, and humans checked them, so text-only systems could also take part.

The task has two subtasks:

  • Subtask A: rank five images according to how well they fit a compound used in a context sentence.
  • Subtask B: choose the image that correctly completes a three-panel sequence and decide whether the sense shown is literal or idiomatic.

The best systems matched human performance. They did this by combining several LLMs and VLMs as a mixture of experts and sending repeated queries. This came at substantial cost, which suggests that models' grasp of idiomaticity is still incomplete.

Key points

  • Static-image data (Subtask A): 100 English and 55 Brazilian Portuguese expressions. Each has five images: two depicting the figurative sense (one strongly, one mildly), two depicting the literal sense (one mildly, one strongly) and an unrelated distractor.
  • Sources of compounds: NCTTI, MAGPIE and FLUTE, plus crowdsourced items. Hyphens that separate idiomatic from literal Portuguese forms (e.g. ovelha-negra) were removed so models could not use them as a shortcut.
  • Subtask A splits: English has 70 training, 15 development and 15 test items. Portuguese has 32, 10 and 13. An extended evaluation set reuses all compounds in their opposite sense and gave more robust results than the small test sets.
  • Sequence data (Subtask B): 30 English items. Each has four candidate final images: the correct ending, two near-miss alternatives and the ending for the other sense.
  • Availability: the data is released under CC-BY-4.0 (doi.org/10.15131/shef.data.28436600.v1). Systems performed better on Portuguese than the organisers expected.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

30 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 26 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{pickard2025semeval,
  title = {{SemEval-2025 Task 1: AdMIRe - Advancing Multimodal Idiomaticity Representation}},
  author = {Pickard, Thomas and Villavicencio, Aline and Mi, Maggie and He, Wei and Phelps, Dylan and Idiart, Marco},
  booktitle = {Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)},
  year = {2025},
  url = {https://aclanthology.org/2025.semeval-1.330/}
}

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  1. The authors would like to acknowledge the contributions of our Brazilian collaborators who made this work possible. In particular, our thanks go to Rozane Rebechi, Juliana Carvalho, Eduardo Victor, César Rennó Costa, Marina Ribeiro and their colleagues and students. We would also like to thank the m… find
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  3. This work was supported by the UKRI AI Centre for Doctoral Training in Speech and Language Technologies (SLT) and their Applications funded by UK Research and Innovation [grant number EP/S023062/1]. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence… find
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