# Characterizing Idioms: Conventionality and Contingency

arXiv: https://arxiv.org/abs/2104.08664 · HTML: https://mlsanity.com/paper/2104.08664/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions](https://mlsanity.com/paper/1802.09961/) (2018)

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

- [Systematicity in GPT-3's Interpretation of Novel English Noun Compounds](https://mlsanity.com/paper/2210.09492/) (2022)
- [Evaluating LLMs on Chinese Idiom Translation](https://mlsanity.com/paper/2508.10421/) (2025)

## Abstract (arXiv)

Idioms are unlike most phrases in two important ways. First, the words in an idiom have non-canonical meanings. Second, the non-canonical meanings of words in an idiom are contingent on the presence of other words in the idiom. Linguistic theories differ on whether these properties depend on one another, as well as whether special theoretical machinery is needed to accommodate idioms. We define two measures that correspond to the properties above, and we implement them using BERT (Devlin et al., 2019) and XLNet(Yang et al., 2019). We show that idioms fall at the expected intersection of the two dimensions, but that the dimensions themselves are not correlated. Our results suggest that special machinery to handle idioms may not be warranted.