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.

• Type: Conference
• Year: 1998
• Where: ACL
• Authors: Ye-Yi Wang, Alex Waibel
• Keywords: ACL 1998, ACL 2007, Statistical Machine Translation, Word-based Alignment, Word-based Alignment Model