Paper: Dependency Forest for Statistical Machine Translation

ACL ID C10-1123
Title Dependency Forest for Statistical Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

We propose a structure called dependency forest for statistical machine translation. A dependency forest compactly represents multiple dependency trees. We develop new algorithms for extracting string-to- dependency rules and training depen- dency language models. Our forest-based string-to-dependency system obtains sig- nificant improvements ranging from 1.36 to 1.46 BLEU points over the tree-based baseline on the NIST 2004/2005/2006 Chinese-English test sets.