PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Bayesian Model of Syntax-Directed Tree to String Grammar Induction
Trevor Cohn and Phil Blunsom
In: EMNLP 2009, 6-7 August 2009, Singapore.

Abstract

Tree based translation models are a compelling means of integrating linguistic in- formation into machine translation. Syntax can inform lexical selection and reordering choices and thereby improve translation quality. Research to date has focussed primarily on decoding with such models, but less on the difficult problem of inducing the bilingual grammar from data. We propose a generative Bayesian model of tree-to-string translation which induces grammars that are both smaller and produce better translations than the previous heuristic two-stage approach which employs a separate word alignment step.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
ID Code:5885
Deposited By:Trevor Cohn
Deposited On:08 March 2010