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 alignment is a major cause of translation errors. We propose a new alignment model based on shallow phrase structures, and the structures can be automatically acquired from parallel corpus. This new model achieved over 10% error reduction for our spoken language translation task.