In this paper, we propose a novel string-todependency algorithm for statistical machine translation. With this new framework, we employ a target dependency language model during decoding to exploit long distance word relations, which are unavailable with a traditional n-gram language model. Our experiments show that the string-to-dependency decoder achieves 1.48 point improvement in BLEU and 2.53 point improvement in TER compared to a standard hierarchical string-tostring system on the NIST 04 Chinese-English evaluation set.
REF make use of target dependency trees and a target dependency language model during decoding.
A New String-to-Dependency Machine Translation Algorithm with a Target Dependency Language Model
Annual Meeting Of The Association For Computational Linguistics
Computer Science