# A Parallel Cross-Lingual Benchmark for Multimodal Idiomaticity Understanding

arXiv: https://arxiv.org/abs/2601.08645 · HTML: https://mlsanity.com/paper/2601.08645/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters](https://mlsanity.com/paper/2402.04252/) (2024)
- [SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding](https://mlsanity.com/paper/2204.10050/) (2022)
- [An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance](https://mlsanity.com/paper/2404.01247/) (2024)
- [AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models](https://mlsanity.com/paper/2109.04413/) (2021)
- [EPIE Dataset: A Corpus For Possible Idiomatic Expressions](https://mlsanity.com/paper/2006.09479/) (2020)
- [IRFL: Image Recognition of Figurative Language](https://mlsanity.com/paper/2303.15445/) (2023)
- [Understanding Figurative Meaning through Explainable Visual Entailment](https://mlsanity.com/paper/2405.01474/) (2024)
- [Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection](https://mlsanity.com/paper/2405.09279/) (2024)
- [Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Context](https://mlsanity.com/paper/2410.16069/) (2024)
- [Construction Artifacts in Metaphor Identification Datasets](https://mlsanity.com/paper/2311.00790/) (2023)
- [Gamified Crowdsourcing for Idiom Corpora Construction](https://mlsanity.com/paper/2102.00881/) (2021)
- [SemEval-2025 Task 1: AdMIRe -- Advancing Multimodal Idiomaticity Representation](https://mlsanity.com/paper/2503.15358/) (2025)

## Built on by (most cited first, 1 of 1)

- [PolyFrame at MWE-2026 AdMIRe 2: When Words Are Not Enough: Multimodal Idiom Disambiguation](https://mlsanity.com/paper/2602.18652/) (2026)

## Abstract (arXiv)

Potentially idiomatic expressions (PIEs) construe meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challenge for assessing the linguistic (and to some extent cultural) capabilities of NLP systems. In this paper, we present XMPIE, a parallel multilingual and multimodal dataset of potentially idiomatic expressions. The dataset, containing 34 languages and over ten thousand items, allows comparative analyses of idiomatic patterns among language-specific realisations and preferences in order to gather insights about shared cultural aspects. This parallel dataset allows to evaluate model performance for a given PIE in different languages and whether idiomatic understanding in one language can be transferred to another. Moreover, the dataset supports the study of PIEs across textual and visual modalities, to measure to what extent PIE understanding in one modality transfers or implies in understanding in another modality (text vs. image). The data was created by language experts, with both textual and visual components crafted under multilingual guidelines, and each PIE is accompanied by five images representing a spectrum from idiomatic to literal meanings, including semantically related and random distractors. The result is a high-quality benchmark for evaluating multilingual and multimodal idiomatic language understanding.