Information-Theoretic Characterization of Vowel Harmony: A Cross-Linguistic Study on Word Lists (2023)

Abstract: We present a cross-linguistic study of vowel harmony that aims to quantifies this phenomenon 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 heavily relied on inflected word-forms in the analysis on vowel harmony. On the contrary, we 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 offering 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.

## introduction 1.vowel harmony many of the world's languages exhibit vowel harmony -a phonological co-occurrence constraint whereby vowels in polysyllabic words have to be members of the same natural class @xcite . natural classes of vowels are defined with respect to polar phonological features such as vowel backness (±back) and roundedness (±round). in a prototypical language with backness, @xmath0, all vowels within a word tend to share @xmath1, i.e. they are either all front (-back) or back (+back). the rather broad application of the term has made it increasingly difficult to define it as a phonological process (cf. @xcite . if vowel harmony is used as a typological feature to group languages into phylogenetic families, this broad application becomes perilous to the researcher since they have to be aware of the the degree of vowel harmonicity in the individual languages. instead of searching for a necessarily complex definition of vowel harmony, research has consequentially concentrated on a quantitative description. ## discussion and conclusion prior work in the (computational) linguistics community has adopted information theory as a framework for the study of human language structure across different linguistic levels including phonology (e.g., @xcite , morphology (e.g., @xcite , and syntax (e.g., @xcite . following the same spirit, we have introduced an information-theoretic metric to quantify vowel harmony based on feature surprisal. our experiments have demonstrated that feature surprisal is a good indicator of whether a certain feature participates in vowel harmony patterns in a language, producing significant differences between harmonic and disharmonic conditions for most harmonic features in five vowel harmony languages. the effect was found on a very small sample of lemma forms with little to no morphological information, showing that large amounts of inflectional data are not necessary to identify some, but not all vowel harmony constraints. when calculated @xmath3, the difference in surprisal was close to zero, meaning the neural language model did not detect any preference for harmony constraints in the languages evaluated. we showed that neural language models can capture non-local harmony constraints over neutral vowels, which is not possible with count-based methods as employed by @xcite or bigram models as in @xcite . here the resolution of the analysis is more finegrained with respect to the features underlying the harmonic groups. the advantage of the modeling approach presented here over both count-based and probabilistic models is that it can be used with a small dataset (word lists of about 1000 word-forms, of which ca. 300 are in the test set as the basis of the actual analysis). the analysis presented could be extended to other types of phonological constraints, since neural language models in theory are able to learn all types of dependencies over sequences of arbitrary length. however, analysing finnish, hungarian, manchu and khalkha mongolian required prior knowledge about harmonic vowels and the split of vowels into harmonic groups, either because the groups are not defined by the value of a feature as is the case for languages with neutral vowels, or because the feature representation in our standardized data itself might not describe a sound with the feature that is assumed to participate in vowel harmony. if it is not known which vowels participate in vowel harmony, it seems best to use information on distinctive features in the data in order to find out which effects can be observed. however, if the vowel harmony patterns are as complex as in khalkha mongolian, the approach presented here would probably find its limits in corpus size. identifying the approximate number of distinct wordforms needed to infer vowel harmony systems of individual language varieties (similar to previous studies inferring the number of words needed to get an approximate account of phoneme numbers, dockum and bowern 2019) would be an interesting topic for future analysis.