# A Survey of Idiom Datasets for Psycholinguistic and Computational Research

arXiv: https://arxiv.org/abs/2508.11828 · HTML: https://mlsanity.com/paper/2508.11828/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 76 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://mlsanity.com/paper/1906.01265/) (2019)
- [Understanding Transformer Memorization Recall Through Idioms](https://mlsanity.com/paper/2210.03588/) (2022)
- [SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding](https://mlsanity.com/paper/2204.10050/) (2022)
- [Examining the Tip of the Iceberg: A Data Set for Idiom Translation](https://mlsanity.com/paper/1802.04681/) (2018)
- [AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models](https://mlsanity.com/paper/2109.04413/) (2021)
- [Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models](https://mlsanity.com/paper/2308.13961/) (2023)
- [LIDIOMS: A Multilingual Linked Idioms Data Set](https://mlsanity.com/paper/1802.08148/) (2018)
- [Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms](https://mlsanity.com/paper/2105.03280/) (2021)
- [Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions](https://mlsanity.com/paper/1802.09961/) (2018)
- [PETCI: A Parallel English Translation Dataset of Chinese Idioms](https://mlsanity.com/paper/2202.09509/) (2022)

## Built on by (most cited first, 2 of 2)

- [When Words Don't Mean What They Say: Figurative Understanding in Bengali Idioms](https://mlsanity.com/paper/2602.12921/) (2026)
- [PolyFrame at MWE-2026 AdMIRe 2: When Words Are Not Enough: Multimodal Idiom Disambiguation](https://mlsanity.com/paper/2602.18652/) (2026)

## Abstract (arXiv)

Idioms are figurative expressions whose meanings often cannot be inferred from their individual words, making them difficult to process computationally and posing challenges for human experimental studies. This survey reviews datasets developed in psycholinguistics and computational linguistics for studying idioms, focusing on their content, form, and intended use. Psycholinguistic resources typically contain normed ratings along dimensions such as familiarity, transparency, and compositionality, while computational datasets support tasks like idiomaticity detection/classification, paraphrasing, and cross-lingual modeling. We present trends in annotation practices, coverage, and task framing across 53 datasets. Although recent efforts expanded language coverage and task diversity, there seems to be no relation yet between psycholinguistic and computational research on idioms.