Paper: Blocked Inference in Bayesian Tree Substitution Grammars

ACL ID P10-2042
Title Blocked Inference in Bayesian Tree Substitution Grammars
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010

Learning a tree substitution grammar is very challenging due to derivational am- biguity. Our recent approach used a Bayesian non-parametric model to induce good derivations from treebanked input (Cohn et al., 2009), biasing towards small grammars composed of small generalis- able productions. In this paper we present a novel training method for the model us- ing a blocked Metropolis-Hastings sam- pler in place of the previous method’s lo- cal Gibbs sampler. The blocked sam- pler makes considerably larger moves than the local sampler and consequently con- verges in less time. A core component of the algorithm is a grammar transforma- tion which represents an infinite tree sub- stitution grammar in a finite context free grammar. This enables efficient blocked inference for training and al...