Probabilistic Typology: Deep Generative Models of Vowel Inventories
This work addresses a foundational question in phonological typology for linguists, offering a novel probabilistic approach that is incremental relative to previous simulation-based methods.
The paper tackled the problem of identifying natural vowel inventories in linguistic typology by introducing deep stochastic point processes, achieving results through experiments on over 200 distinct languages.
Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example, all languages have vowels, while most---but not all---languages have an /u/ sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages.