# Morphological Irregularity Correlates with Frequency

arXiv: https://arxiv.org/abs/1906.11483 · HTML: https://mlsanity.com/paper/1906.11483/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 13 of 13)

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- [Complexity counts: global and local perspectives on Indo-Aryan numeral systems](https://mlsanity.com/paper/2505.21510/) (2025)
- [Systematicity between Forms and Meanings across Languages Supports Efficient Communication](https://mlsanity.com/paper/2601.17181/) (2026)
- [When Irregularity Helps: A Subclass Analysis of Inductive Bias in Neural Morphology](https://mlsanity.com/paper/2605.20558/) (2026)
- [Discovering Properties of Inflectional Morphology in Neural Emergent Communication](https://mlsanity.com/paper/2508.05843/) (2025)
- [How communicatively optimal are exact numeral systems? Once more on lexicon size and morphosyntactic complexity](https://mlsanity.com/paper/2602.20372/) (2026)
- [Not All Irregularity Is Equal: Causally Isolating a Rare Failure Mode in Japanese Morphological Inflection](https://mlsanity.com/paper/2609.21179/) (2026)

## Abstract (arXiv)

We present a study of morphological irregularity. Following recent work, we define an information-theoretic measure of irregularity based on the predictability of forms in a language. Using a neural transduction model, we estimate this quantity for the forms in 28 languages. We first present several validatory and exploratory analyses of irregularity. We then show that our analyses provide evidence for a correlation between irregularity and frequency: higher frequency items are more likely to be irregular and irregular items are more likely be highly frequent. To our knowledge, this result is the first of its breadth and confirms longstanding proposals from the linguistics literature. The correlation is more robust when aggregated at the level of whole paradigms--providing support for models of linguistic structure in which inflected forms are unified by abstract underlying stems or lexemes. Code is available at https://github.com/shijie-wu/neural-transducer.