# EPIE Dataset: A Corpus For Possible Idiomatic Expressions

arXiv: https://arxiv.org/abs/2006.09479 · HTML: https://mlsanity.com/paper/2006.09479/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 24 of 24)

- [Understanding Transformer Memorization Recall Through Idioms](https://mlsanity.com/paper/2210.03588/) (2022)
- [Do GPTs Produce Less Literal Translations?](https://mlsanity.com/paper/2305.16806/) (2023)
- [Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models](https://mlsanity.com/paper/2308.13961/) (2023)
- [Improving LLM Abilities in Idiomatic Translation](https://mlsanity.com/paper/2407.03518/) (2024)
- [Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms](https://mlsanity.com/paper/2105.03280/) (2021)
- [Semantics of Multiword Expressions in Transformer-Based Models: A Survey](https://mlsanity.com/paper/2401.15393/) (2024)
- [Memorization or Reasoning? Exploring the Idiom Understanding of LLMs](https://mlsanity.com/paper/2505.16216/) (2025)
- [IBERT: Idiom Cloze-style reading comprehension with Attention](https://mlsanity.com/paper/2112.02994/) (2021)
- [Using large language models to estimate features of multi-word expressions: Concreteness, valence, arousal](https://mlsanity.com/paper/2408.16012/) (2024)
- [Isolating Culture Neurons in Multilingual Large Language Models](https://mlsanity.com/paper/2508.02241/) (2025)
- [That was the last straw, we need more: Are Translation Systems Sensitive to Disambiguating Context?](https://mlsanity.com/paper/2310.14610/) (2023)
- [NLP Datasets for Idiom and Figurative Language Tasks](https://mlsanity.com/paper/2511.16345/) (2025)
- [CLIX: Cross-Lingual Explanations of Idiomatic Expressions](https://mlsanity.com/paper/2501.03191/) (2025)
- [Evaluating the Impact of Verbal Multiword Expressions on Machine Translation](https://mlsanity.com/paper/2508.17458/) (2025)
- [A Parallel Cross-Lingual Benchmark for Multimodal Idiomaticity Understanding](https://mlsanity.com/paper/2601.08645/) (2026)
- [SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech](https://mlsanity.com/paper/2602.09866/) (2026)
- [IdEALS: Idiomatic Expressions for Advancement of Language Skills](https://mlsanity.com/paper/2305.13637/) (2023)
- [Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling](https://mlsanity.com/paper/2406.08218/) (2024)
- [Large Language Models for Persian  leftrightarrow  English Idiom Translation](https://mlsanity.com/paper/2412.09993/) (2024)
- [Span Modeling for Idiomaticity and Figurative Language Detection with Span Contrastive Loss](https://mlsanity.com/paper/2603.22799/) (2026)
- [Exploring Concreteness Through a Figurative Lens](https://mlsanity.com/paper/2604.18296/) (2026)
- [SiNFluD: Creating and Evaluating Figurative Language Dataset for Sindhi](https://mlsanity.com/paper/2605.01323/) (2026)
- [IdioLink: Retrieving Meaning Beyond Words Across Idiomatic and Literal Expressions](https://mlsanity.com/paper/2605.22247/) (2026)
- [G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment](https://mlsanity.com/paper/2606.18989/) (2026)

## Abstract (arXiv)

Idiomatic expressions have always been a bottleneck for language comprehension and natural language understanding, specifically for tasks like Machine Translation(MT). MT systems predominantly produce literal translations of idiomatic expressions as they do not exhibit generic and linguistically deterministic patterns which can be exploited for comprehension of the non-compositional meaning of the expressions. These expressions occur in parallel corpora used for training, but due to the comparatively high occurrences of the constituent words of idiomatic expressions in literal context, the idiomatic meaning gets overpowered by the compositional meaning of the expression. State of the art Metaphor Detection Systems are able to detect non-compositional usage at word level but miss out on idiosyncratic phrasal idiomatic expressions. This creates a dire need for a dataset with a wider coverage and higher occurrence of commonly occurring idiomatic expressions, the spans of which can be used for Metaphor Detection. With this in mind, we present our English Possible Idiomatic Expressions(EPIE) corpus containing 25206 sentences labelled with lexical instances of 717 idiomatic expressions. These spans also cover literal usages for the given set of idiomatic expressions. We also present the utility of our dataset by using it to train a sequence labelling module and testing on three independent datasets with high accuracy, precision and recall scores.