@InProceedings{cotterell-eisner:2017:Long,
  author    = {Cotterell, Ryan  and  Eisner, Jason},
  title     = {Probabilistic Typology: Deep Generative Models of Vowel Inventories},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1182--1192},
  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.},
  url       = {http://aclweb.org/anthology/P17-1109}
}