A dependency treelet string correspondence model for statistical machine translation

This paper describes a novel model using dependency structures on the source side for syntax-based statistical machine transla-tion: Dependency Treelet String Correspon-dence Model (DTSC). The DTSC model maps source dependency structures to tar-get strings. In this model translation pairs of source treelets and target strings with their word alignments are learned automatically from the parsed and aligned corpus. The DTSC model allows source treelets and tar-get strings with variables so that the model can generalize to handle dependency struc-tures with the same head word but with dif-ferent modifiers and arguments. Addition-ally, target strings can be also discontinuous by using gaps which are corresponding to the uncovered nodes which are not included in the source treelets. A chart-style decod-ing algorithm with two basic operations– substituting and attaching–is designed for the DTSC model. We argue that the DTSC model proposed here is capable of lexical-ization, generalization, and handling discon-tinuous phrases which are very desirable for machine translation. We finally evaluate our current implementation of a simplified ver-sion of DTSC for statistical machine trans-lation.