  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.
