Paper: Learning to Transform and Select Elementary Trees for Improved Syntax-based Machine Translations

ACL ID P11-1085
Title Learning to Transform and Select Elementary Trees for Improved Syntax-based Machine Translations
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We propose a novel technique of learning how to transform the source parse trees to improve the trans- lation qualities of syntax-based translation mod- els using synchronous context-free grammars. We transform the source tree phrasal structure into a set of simpler structures, expose such decisions to the decoding process, and find the least expensive transformation operation to better model word re- ordering. In particular, we integrate synchronous bi- narizations, verb regrouping, removal of redundant parse nodes, and incorporate a few important fea- tures such as translation boundaries. We learn the structural preferences from the data in a generative framework. The syntax-based translation system in- tegrating the proposed techniques outperforms the best Arabic-English unconstrained s...