Abstract
A translation model based on synchronous tree-substitution-grammar is presented in this paper.It can elegantly model the global reordering and discontinuous phrases.Furthermore,it can learn non-isomorphic tree-to-tree mappings.Experimental results on two different data sets show that the proposed model significantly outperforms the phrase-based model and the model based on synchronous context-free grammar.
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