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Scalable Inference and Training of Context-Rich Syntactic Translation Models

Galley, Michel; Graehl, Jonathan; Knight, Kevin; Marcu, Daniel; DeNeefe, Steve; Wang, Wei; Thayer, Ignacio

Statistical MT has made great progress in the last few years, but current translation models are weak on re-ordering and target language fluency. Syn- tactic approaches seek to remedy these problems. In this paper, we take the framework for acquiring multi-level syntactic translation rules of (Galley et al., 2004) from aligned tree-string pairs, and present two main extensions of their approach: first, instead of merely computing a single derivation that minimally explains a sentence pair, we construct a large number of derivations that include contextually richer rules, and account for multiple interpretations of unaligned words. Second, we pro- pose probability estimates and a training procedure for weighting these rules. We contrast different approaches on real examples, show that our estimates based on multiple derivations favor phrasal re-orderings that are linguistically better motivated, and establish that our larger rules provide a 3.63 BLEU point increase over minimal rules.


More About This Work

Academic Units
Computer Science
Proceedings of COLING/ACL
Published Here
June 30, 2013