@inproceedings{arslan-etal-2026-mwe,
title = "{MWE}-2026 Shared Task: {A}d{MIR}e 2 Advancing Multimodal Idiomaticity Representation",
author = {Arslan, Do{\u{g}}ukan and
Wilkens, Rodrigo and
He, Wei and
Selamet, Dilara Torunoglu and
Pickard, Thomas and
Villavicencio, Aline and
Pagano, Adriana Silvina and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
editor = {Ojha, Atul Kr. and
Mititelu, Verginica Barbu and
Constant, Mathieu and
Stoyanova, Ivelina and
Do{\u{g}}ru{\"o}z, A. Seza and
Rademaker, Alexandre},
booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
month = mar,
year = "2026",
address = "Rabat, Marocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mwe-1.34/",
pages = "276--287",
ISBN = "979-8-89176-363-0",
abstract = "Idiomatic expressions present a unique chal-lenge in NLP, as their meanings are often notdirectly inferable from their constituent words.Despite recent advancements in large languagemodels, idiomaticity remains a significant ob-stacle to robust semantic representation. Wepresent datasets and task results for MWE-2026 Shared Task 2: Advancing MultimodalIdiomaticity Representation 2 (AdMIRe 2),which challenges the community to assess andimprove models' ability to interpret idiomaticexpressions in multimodal contexts across mul-tiple languages. Participants competed in animage ranking task in which, for each item,systems receive a context sentence containinga potentially idiomatic expression (PIE) andfive candidate images. Participating systemsare required to predict the sentence type (i.e.,idiomatic vs. literal) for the given context andrank the images by how well they depict the in-tended meaning in that context. Among the par-ticipating systems the most effective methodsinclude pipelines utilizing closed-source com-mercial models such as Gemini 2.5 and GPT-5, and employing chain-of-thought reasoningstrategies. Methods to mitigate language mod-els' bias towards literal interpretations and en-sembles to smooth out variance were common."
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<abstract>Idiomatic expressions present a unique chal-lenge in NLP, as their meanings are often notdirectly inferable from their constituent words.Despite recent advancements in large languagemodels, idiomaticity remains a significant ob-stacle to robust semantic representation. Wepresent datasets and task results for MWE-2026 Shared Task 2: Advancing MultimodalIdiomaticity Representation 2 (AdMIRe 2),which challenges the community to assess andimprove models’ ability to interpret idiomaticexpressions in multimodal contexts across mul-tiple languages. Participants competed in animage ranking task in which, for each item,systems receive a context sentence containinga potentially idiomatic expression (PIE) andfive candidate images. Participating systemsare required to predict the sentence type (i.e.,idiomatic vs. literal) for the given context andrank the images by how well they depict the in-tended meaning in that context. Among the par-ticipating systems the most effective methodsinclude pipelines utilizing closed-source com-mercial models such as Gemini 2.5 and GPT-5, and employing chain-of-thought reasoningstrategies. Methods to mitigate language mod-els’ bias towards literal interpretations and en-sembles to smooth out variance were common.</abstract>
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%0 Conference Proceedings
%T MWE-2026 Shared Task: AdMIRe 2 Advancing Multimodal Idiomaticity Representation
%A Arslan, Doğukan
%A Wilkens, Rodrigo
%A He, Wei
%A Selamet, Dilara Torunoglu
%A Pickard, Thomas
%A Villavicencio, Aline
%A Pagano, Adriana Silvina
%A Eryiğit, Gülşen
%Y Ojha, Atul Kr.
%Y Mititelu, Verginica Barbu
%Y Constant, Mathieu
%Y Stoyanova, Ivelina
%Y Doğruöz, A. Seza
%Y Rademaker, Alexandre
%S Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-363-0
%F arslan-etal-2026-mwe
%X Idiomatic expressions present a unique chal-lenge in NLP, as their meanings are often notdirectly inferable from their constituent words.Despite recent advancements in large languagemodels, idiomaticity remains a significant ob-stacle to robust semantic representation. Wepresent datasets and task results for MWE-2026 Shared Task 2: Advancing MultimodalIdiomaticity Representation 2 (AdMIRe 2),which challenges the community to assess andimprove models’ ability to interpret idiomaticexpressions in multimodal contexts across mul-tiple languages. Participants competed in animage ranking task in which, for each item,systems receive a context sentence containinga potentially idiomatic expression (PIE) andfive candidate images. Participating systemsare required to predict the sentence type (i.e.,idiomatic vs. literal) for the given context andrank the images by how well they depict the in-tended meaning in that context. Among the par-ticipating systems the most effective methodsinclude pipelines utilizing closed-source com-mercial models such as Gemini 2.5 and GPT-5, and employing chain-of-thought reasoningstrategies. Methods to mitigate language mod-els’ bias towards literal interpretations and en-sembles to smooth out variance were common.
%U https://aclanthology.org/2026.mwe-1.34/
%P 276-287
Markdown (Informal)
[MWE-2026 Shared Task: AdMIRe 2 Advancing Multimodal Idiomaticity Representation](https://aclanthology.org/2026.mwe-1.34/) (Arslan et al., MWE 2026)
ACL
- Doğukan Arslan, Rodrigo Wilkens, Wei He, Dilara Torunoglu Selamet, Thomas Pickard, Aline Villavicencio, Adriana Silvina Pagano, and Gülşen Eryiğit. 2026. MWE-2026 Shared Task: AdMIRe 2 Advancing Multimodal Idiomaticity Representation. In Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026), pages 276–287, Rabat, Marocco. Association for Computational Linguistics.