A comparison of syntactic reordering methods for english-german machine translation

Abstract

We describe two methods for syntactic source reordering developed for English-German SMT. Both methods learn from bilingual data accompanied by automatic word alignments to reorder the source such that it resembles that of the target. While the first method is an extension of a parse-based algorithm and accommodates contextual triggers in the parse, the second method uses a linear feature-based cost model along with a Traveling Salesman Problem (TSP) solver to perform the reordering. Our results indicate that both methods lead to improvements in BLEU scores in both directions, English→German and German→English. Significant gains in human translation quality assessment are observed for German→English, however, no significant changes are observed in the human assessment for English→German. © 2012 The COLING.