Modeling with structures in statistical machine translation

Most statistical machine translation systems employ a word-based alignment model. In this paper we demonstrate that word-based align-ment is a major cause of translation errors. We propose a new alignment model based on shal-low phrase structures, and the structures can be automatically acquired from parallel corpus. This new model achieved over 10 % error reduc-tion for our spoken language translation task. 1 In t roduct ion Most (if not all) statistical machine translation systems employ a word-based alignment model (Brown et al., 1993; Vogel, Ney, and Tillman, 1996; Wang and Waibel, 1997), which treats words in a sentence as independent entities an