Learning stochastic OT grammars: a Bayesian approach using data augmentation and Gibbs sampling

  • Authors:

  • Ying Lin

  • Affiliations:

  • University of California, Los Angeles, Los Angeles, CA

  • Venue:
  • ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2005

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Abstract

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.