Paper: Modeling with Structures in Statistical Machine translation

ACL ID P98-2221
Title Modeling with Structures in Statistical Machine translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

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.