Paper: Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation

ACL ID D08-1010
Title Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

This paper proposes a novel maximum en- tropy based rule selection (MERS) model for syntax-based statistical machine transla- tion (SMT). The MERS model combines lo- cal contextual information around rules and information of sub-trees covered by variables in rules. Therefore, our model allows the de- coder to perform context-dependent rule se- lection during decoding. We incorporate the MERS model into a state-of-the-art linguis- tically syntax-based SMT model, the tree- to-string alignment template model. Experi- ments show that our approach achieves signif- icant improvements over the baseline system.