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<Paper uid="P05-1043">
  <Title>Learning Stochastic OT Grammars: A Bayesian approach using Data Augmentation and Gibbs Sampling</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
Stochastic Optimality Theory (Boersma,
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
    <Paragraph position="1"> This Gibbs sampler constructs a Markov chain that converges to the joint distribution, and the target posterior can be derived as its marginal distribution.</Paragraph>
  </Section>
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