Chinese syntactic reordering for statistical machine translation

Syntactic reordering approaches are an ef-fective method for handling word-order dif-ferences between source and target lan-guages in statistical machine translation (SMT) systems. This paper introduces a re-ordering approach for translation from Chi-nese to English. We describe a set of syntac-tic reordering rules that exploit systematic differences between Chinese and English word order. The resulting system is used as a preprocessor for both training and test sentences, transforming Chinese sentences to be much closer to English in terms of their word order. We evaluated the reordering approach within the MOSES phrase-based SMT system (Koehn et al., 2007). The reordering approach improved the BLEU score for the MOSES system from 28.52 to 30.86 on the NIST 2006 evaluation data. We also conducted a series of experiments to an-alyze the accuracy and impact of different types of reordering rules.