Meta-Structure Transformation Model for Statistical Machine Translation

We propose a novel syntax-based model for statistical machine translation in which meta-structure (MS) and meta-structure sequence (SMS) of a parse tree are defined. In this framework, a parse tree is decomposed into SMS to deal with the structure divergence and the alignment can be reconstructed at different levels of recombination of MS (RM). RM pairs extracted can perform the mapping between the substructures across languages. As a result, we have got not only the translation for the target language, but an SMS of its parse tree at the same time. Experiments with BLEU metric show that the model significantly outperforms Pharaoh, a state-art-theart phrase-based system.