Paper: A Bayesian Model of Syntax-Directed Tree to String Grammar Induction

ACL ID D09-1037
Title A Bayesian Model of Syntax-Directed Tree to String Grammar Induction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

Tree based translation models are a com- pelling means of integrating linguistic in- formation into machine translation. Syn- tax can inform lexical selection and re- ordering 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 pro- duce better translations than the previous heuristic two-stage approach which em- ploys a separate word alignment step.