acl acl2012 acl2012-147 acl2012-147-reference knowledge-graph by maker-knowledge-mining
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Author: Deyi Xiong ; Min Zhang ; Haizhou Li
Abstract: 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 transla- , tion tasks with large-scale training data. Experimental results demonstrate that the two models significantly improve translation accuracy.
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