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 predicate-argument 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-the-art 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.
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