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 point improvement in BLEU and point improvement in TER compared to a standard hierarchical string-tostring system on the NIST 04 Chinese-English evaluation set. .
