Probabilistic Typology: Deep Generative Models of Vowel Inventories

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.

[1]  Liang Zhou,et al.  Modeling and Characterizing Social Media Topics Using the Gamma Distribution , 2015, EVENTS@HLP-NAACL.

[2]  Ben Taskar,et al.  Learning Determinantal Point Processes , 2011, UAI.

[3]  Viveka Velupillai,et al.  An Introduction to Linguistic Typology , 2012 .

[4]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[5]  B. Lindblom Phonetic Universals in Vowel Systems , 1986 .

[6]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[7]  Paul Boersma,et al.  Praat, a system for doing phonetics by computer , 2002 .

[8]  J. Schwartz,et al.  The Dispersion-Focalization Theory of vowel systems , 1997 .

[9]  Wilson L. Taylor,et al.  “Cloze Procedure”: A New Tool for Measuring Readability , 1953 .

[10]  Idit Keidar,et al.  Introduction , 2019, Concurrency: the Works of Leslie Lamport.

[11]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[12]  B. Lindblom,et al.  Numerical Simulation of Vowel Quality Systems: The Role of Perceptual Contrast , 1972 .

[13]  Alex Kulesza,et al.  Markov Determinantal Point Processes , 2012, UAI.

[14]  Kenneth N. Stevens,et al.  On the quantal nature of speech , 1972 .

[15]  John W. Fisher,et al.  Estimating the Partition Function by Discriminance Sampling , 2015, UAI.

[16]  P. Ladefoged A course in phonetics , 1975 .

[17]  E. Rains,et al.  Eynard–Mehta Theorem, Schur Process, and their Pfaffian Analogs , 2004, math-ph/0409059.

[18]  R. Jakobson Kindersprache, Aphasie und allgemeine Lautgesetze , 1942 .

[19]  O. Macchi The coincidence approach to stochastic point processes , 1975, Advances in Applied Probability.

[20]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Ben Taskar,et al.  Determinantal Point Processes for Machine Learning , 2012, Found. Trends Mach. Learn..

[22]  P. Ladefoged,et al.  The sounds of the world's languages , 1996 .

[23]  J. B. Pickering,et al.  Vowel Perception and Production , 1994 .

[24]  Ulrich Paquet,et al.  Low-Rank Factorization of Determinantal Point Processes , 2017, AAAI.

[25]  E. Ising Beitrag zur Theorie des Ferromagnetismus , 1925 .

[26]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[27]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[28]  van Marie-Colette Lieshout,et al.  Markov Point Processes and Their Applications , 2000 .

[29]  Anna Korhonen,et al.  Improved Lexical Acquisition through DPP-based Verb Clustering , 2013, ACL.

[30]  Roy Becker-Kristal,et al.  Acoustic typology of vowel inventories and Dispersion Theory: Insights from a large cross-linguistic corpus , 2010 .

[31]  Kalina Bontcheva,et al.  Point Process Modelling of Rumour Dynamics in Social Media , 2015, ACL.

[32]  Larry M. Hyman What is Phonological Typology , 2014 .

[33]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..