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We present a cross-linguistic study that aims to quantify vowel harmony usingdata-driven computational modeling. Concretely, we define aninformation-theoretic measure of harmonicity based on the predictability ofvowels in a natural language lexicon, which we estimate using phoneme-levellanguage models (PLMs). Prior quantitative studies have relied heavily oninflected word-forms in the analysis of vowel harmony. We instead train ourmodels using cross-linguistically comparable lemma forms with little or noinflection, which enables us to cover more under-studied languages. Trainingdata for our PLMs consists of word lists with a maximum of 1000 entries perlanguage. Despite the fact that the data we employ are substantially smallerthan previously used corpora, our experiments demonstrate the neural PLMscapture vowel harmony patterns in a set of languages that exhibit thisphenomenon. Our work also demonstrates that word lists are a valuable resourcefor typological research, and offers new possibilities for future studies onlow-resource, under-studied languages.

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