Paper: A Linguistically Annotated Reordering Model for BTG-based Statistical Machine Translation

ACL ID P08-2038
Title A Linguistically Annotated Reordering Model for BTG-based Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

In this paper, we propose a linguistically anno- tated reordering model for BTG-based statis- tical machine translation. The model incorpo- rates linguistic knowledge to predict orders for both syntactic and non-syntactic phrases. The linguistic knowledge is automatically learned from source-side parse trees through an an- notation algorithm. We empirically demon- strate that the proposed model leads to a sig- nificant improvement of 1.55% in the BLEU score over the baseline reordering model on the NIST MT-05 Chinese-to-English transla- tion task.