Statistical machine translation model based on a synchronous tree-substitution grammar

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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. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

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Jiang, H. F., Li, S., Fu, G. H., Zhao, T. J., & Zhang, M. (2009). Statistical machine translation model based on a synchronous tree-substitution grammar. Ruan Jian Xue Bao/Journal of Software, 20(5), 1241–1253. https://doi.org/10.3724/SP.J.1001.2009.03409

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