IdiomX A Multilingual Benchmark for Idiom Understanding, Retrieval, and Interpretation

[2606.02584] IdiomX A Multilingual Benchmark for Idiom Understanding, Retrieval, and Interpretation

[Submitted on 25 Apr 2026]

Title:IdiomX A Multilingual Benchmark for Idiom Understanding, Retrieval, and Interpretation

View a PDF of the paper titled IdiomX A Multilingual Benchmark for Idiom Understanding, Retrieval, and Interpretation, by Ayman Ali Sharara

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Abstract:Idiomatic expressions remain a persistent challenge for natural language processing because their meanings are often non-compositional, context-dependent, and difficult to align across languages. Existing idiom resources are often limited in scale, contextual diversity, or multilingual coverage, restricting their utility for modern language models. We introduce IdiomX, a large-scale multilingual benchmark for idiom understanding, retrieval, and interpretation, constructed through a reproducible multi-stage pipeline combining lexical resource extraction, large-scale normalization, controlled large language model enrichment, and structured validation. The resulting dataset contains over 190K contextualized examples spanning 12K+ idioms, with aligned English, Arabic, and French semantic representations, idiomatic and literal usage labels, and rich linguistic metadata. Building on this resource, we define a unified four-task benchmark covering idiom detection, context-to-idiom retrieval, Arabic-to-English idiom retrieval, and idiom interpretation, extending evaluation from figurative recognition to semantic grounding and explainable meaning retrieval. Experiments show that contextual transformer models substantially improve idiom detection, while hybrid retrieval and reranking architectures significantly strengthen both monolingual and cross-lingual idiom retrieval. Results further demonstrate that idiom interpretation can be effectively modeled as a semantic retrieval task, introducing interpretability as a complementary benchmark dimension. Overall, IdiomX provides a scalable benchmark for studying idiomatic language as a progression from detection to retrieval and semantic interpretation, and offers a modular framework extensible to additional languages and figurative reasoning tasks

Comments: 12 pages, 21 figures. Includes dataset and code. Resources available on HuggingFace, Kaggle, and GitHub

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

ACM classes: I.2.7; I.2.6

Cite as: arXiv:2606.02584 [cs.CL]

(or arXiv:2606.02584v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.02584

arXiv-issued DOI via DataCite

Submission history

From: Ayman Sharara Mr. [view email] [v1] Sat, 25 Apr 2026 19:54:34 UTC (1,016 KB)

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