What conditions tone paradigms in Yukuna: Phonological and machine learning approaches

Abstract

Yukuna is an understudied Arawak language of North-West Amazonia with a privative tonal system. In this system, roots are underlyingly specified for tone, whilst affixes are toneless. However, affixation interacts with tone, leading to many variations in surface tonal patterns. This paper puts forth a qualitative analysis of Yukuna’s tonal system, and provides data-driven evidence in favor of this analysis using machine learning methods. More precisely, we use decision trees and random forests to assess quantitatively the predictions of the phonological analysis. A manually annotated corpus of verbal paradigms was split into a training and a testing set. We trained the computational classifiers on the first and tested their predictions on the second. We found that they predict the majority of the patterns and support the qualitative analysis. Additionally, they suggest avenues for enhancing the phonological analysis, by providing a ranking of the variables that highlight statistical tendencies within tonal patterns. Besides its contribution to understanding tonal systems in general and of that of Yukuna in particular, our work also suggests that such machine learning approaches might become part of the complex theoretical and methodological toolkit needed for language description and linguistic theory development.

Keywords

• Yukuna
• tone
• machine learning
• decision tree