PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Blocked inference in Bayesian tree substitution grammars
Trevor Cohn and Phil Blunsom
In: ACL 2010, 11-16 July 2010, Uppsala, Sweden.

Abstract

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 also improves the parsing algorithm. Both algorithms are shown to improve parsing accuracy.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7946
Deposited By:Phil Blunsom
Deposited On:17 March 2011