Probabilistic Typology: Deep Generative Models of Vowel Inventories

Ryan Cotterell, Jason Eisner


Abstract
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.
Anthology ID:
P17-1109
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1182–1192
Language:
URL:
https://aclanthology.org/P17-1109
DOI:
10.18653/v1/P17-1109
Bibkey:
Cite (ACL):
Ryan Cotterell and Jason Eisner. 2017. Probabilistic Typology: Deep Generative Models of Vowel Inventories. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1182–1192, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Probabilistic Typology: Deep Generative Models of Vowel Inventories (Cotterell & Eisner, ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1109.pdf
Video:
 https://aclanthology.org/P17-1109.mp4