Predicate-argument structure contains rich semantic information of which statistical machine translation hasn't taken full advantage. In this paper, we propose two discriminative, feature-based models to exploit predicateargument structures for statistical machine translation: 1) a predicate translation model and 2) an argument reordering model. The predicate translation model explores lexical and semantic contexts surrounding a verbal predicate to select desirable translations for the predicate. The argument reordering model automatically predicts the moving direction of an argument relative to its predicate after translation using semantic features. The two models are integrated into a state-of-theart phrase-based machine translation system and evaluated on Chinese-to-English translation tasks with large-scale training data. Experimental results demonstrate that the two models significantly improve translation accuracy.
In addition, REF proposed an argument reordering model to predict the relative position between predicates and arguments.
Modeling the Translation of Predicate-Argument Structure for SMT
ACL
Computer Science