Reordering is currently one of the most important problems in statistical machine translation systems. This paper presents a novel strategy for dealing with it: statistical machine reordering (SMR). It consists in using the powerful techniques developed for statistical machine translation (SMT) to translate the source language (S) into a reordered source language (S'), which allows for an improved translation into the target language (T). The SMT task changes from S2T to S'2T which leads to a monotonized word alignment and shorter translation units. In addition, the use of classes in SMR helps to infer new word reorderings. Experiments are reported in the EsEn WMT06 tasks and the ZhEn IWSLT05 task and show significant improvement in translation quality.
REF view the source reordering as a translation task that translate the source language into a reordered source language.
Statistical Machine Reordering
Conference On Empirical Methods In Natural Language Processing
Computer Science