Paper: Maximum Entropy Based Phrase Reordering for Hierarchical Phrase-Based Translation

ACL ID D10-1054
Title Maximum Entropy Based Phrase Reordering for Hierarchical Phrase-Based Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Hierarchical phrase-based (HPB) translation provides a powerful mechanism to capture both short and long distance phrase reorder- ings. However, the phrase reorderings lack of contextual information in conventional HPB systems. This paper proposes a context- dependent phrase reordering approach that uses the maximum entropy (MaxEnt) model to help the HPB decoder select appropriate re- ordering patterns. We classify translation rules into several reordering patterns, and build a MaxEnt model for each pattern based on var- ious contextual features. We integrate the MaxEnt models into the HPB model. Ex- perimental results show that our approach achieves significant improvements over a stan- dard HPB system on large-scale translation tasks. On Chinese-to-English translation, the absolute impro...