Soft Dependency Matching for Hierarchical Phrase-based Machine Translation

This paper proposes a soft dependency matching model for hierarchical phrase-based (HPB) machine translation. When a HPB rule is extracted, we enrich it with dependency knowledge automatically learnt from the training data. The dependency knowledge not only encodes the dependency relations between the components inside the rule, but also contains the dependency relations between the rule and its context. When a rule is applied to translate a sentence, the dependency knowledge is used to compute the syntactic structural consistency of the rule against the dependency tree of the sentence. We characterize the structure consistency by three features and integrate them into the standard SMT log-linear model to guide the translation process. Our method is evaluated on multiple Chinese-to-English machine translation test sets. The experimental results show that our soft matching model achieves 0.7-1.4 BLEU points improvements over a strong baseline of an in-house implemented HPB translation system.