Moreover, the proposed method yields 0.96 BLEU improvement relative to supervised word segmenters on an out-of-domain corpus.
we believe that the proposed method would benefit SMT related to low-resource languages where annotated data are scare, and would also find application in domains that differ too greatly from the domains on which supervised word segmenters were trained.