# Information-Theoretic Characterization of Vowel Harmony: A Cross-Linguistic Study on Word Lists

arXiv: https://arxiv.org/abs/2308.04885 · HTML: https://mlsanity.com/paper/2308.04885/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Phonotactic Complexity and its Trade-offs](https://mlsanity.com/paper/2005.03774/) (2020)
- [A surprisal--duration trade-off across and within the world's languages](https://mlsanity.com/paper/2109.15000/) (2021)
- [Morphological Irregularity Correlates with Frequency](https://mlsanity.com/paper/1906.11483/) (2019)
- [Disambiguatory Signals are Stronger in Word-initial Positions](https://mlsanity.com/paper/2102.02183/) (2021)

## Built on by (most cited first, 4 of 4)

- [Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent](https://mlsanity.com/paper/2505.07659/) (2025)
- [Tonguescape: Exploring Language Models Understanding of Vowel Articulation](https://mlsanity.com/paper/2501.17643/) (2025)
- [Phonotactic Complexity across Dialects](https://mlsanity.com/paper/2402.12998/) (2024)
- [Probing Subphonemes in Morphology Models](https://mlsanity.com/paper/2505.11297/) (2025)

## Abstract (arXiv)

We present a cross-linguistic study that aims to quantify vowel harmony using data-driven computational modeling. Concretely, we define an information-theoretic measure of harmonicity based on the predictability of vowels in a natural language lexicon, which we estimate using phoneme-level language models (PLMs). Prior quantitative studies have relied heavily on inflected word-forms in the analysis of vowel harmony. We instead train our models using cross-linguistically comparable lemma forms with little or no inflection, which enables us to cover more under-studied languages. Training data for our PLMs consists of word lists with a maximum of 1000 entries per language. Despite the fact that the data we employ are substantially smaller than previously used corpora, our experiments demonstrate the neural PLMs capture vowel harmony patterns in a set of languages that exhibit this phenomenon. Our work also demonstrates that word lists are a valuable resource for typological research, and offers new possibilities for future studies on low-resource, under-studied languages.