2017
ACL
ACL 2017
Probabilistic Typology: Deep Generative Models of Vowel Inventories
Abstract
AbstractLinguistic 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.
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Interdisciplinary Bridge
— Deep Learning and Interdisciplinary and Machine Learning
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Keyword Pioneer
— stochastic point process
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Hot Topic Early Bird
— probabilistic modeling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Stochastic Processes
Deep Learning > Models > Generative Models
Interdisciplinary > Linguistics
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Learning Types > Probabilistic Modeling