A Gibbs sampler for phrasal synchronous grammar induction

Abstract

We present a phrasal synchronous grammar model of translational equivalence. Unlike previous approaches, we do not resort to heuristics or constraints from a word-alignment model, but instead directly induce a synchronous grammar from parallel sentence-aligned corpora. We use a hierarchical Bayesian prior to bias towards compact grammars with small translation units. Inference is performed using a novel Gibbs sampler over synchronous derivations. This sampler side-steps the intractability issues of previous models which required inference over derivation forests. Instead each sampling iteration is highly efficient, allowing the model to be applied to larger translation corpora than previous approaches.