Learning Stochastic OT Grammars: A Bayesian approach using Data Augmentation and Gibbs Sampling

摘要

Stochastic Optimality Theory (Boersma, 1997) is a widely-used model in linguistics that did not have a theoretically sound learning method previously. In this paper, a Markov chain Monte-Carlo method is proposed for learning Stochastic OT Grammars. Following a Bayesian framework, the goal is finding the posterior distribution of the grammar given the relative frequencies of input-output pairs. The Data Augmentation algorithm allows one to simulate a joint posterior distribution by iterating two conditional sampling steps. This Gibbs sampler constructs a Markov chain that converges to the joint distribution, and the target posterior can be derived as its marginal distribution.