Paper: Statistical Machine Reordering

ACL ID W06-1609
Title Statistical Machine Reordering
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006

Reordering is currently one of the most important problems in statistical machine translation systems. This paper presents a novel strategy for dealing with it: sta- tistical machine reordering (SMR). It con- sists in using the powerful techniques de- veloped 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 signi cant im- provement in translation quality.