A Hybrid Approach to Skeleton-based Translation

Tong Xiao, Jingbo Zhu, Chunliang Zhang

Abstract: In this paper we explicitly consider sentence skeleton information for Machine Translation (MT). The basic idea is that we translate the key elements of the input sentence using a skeleton translation model, and then cover the remain segments using a full translation model. We apply our approach to a state-of-the-art phrase-based system and demonstrate very promising BLEU improvements and TER reductions on the NIST Chinese-English MT evaluation data.