A Statistical Machine Translation Model Based on a Synthetic Synchronous Grammar

Recently, various synchronous grammars are proposed for syntax-based machine translation, e.g. synchronous context-free grammar and synchronous tree (sequence) substitution grammar, either purely formal or linguistically motivated. Aiming at combining the strengths of different grammars, we describes a synthetic synchronous grammar (SSG), which tentatively in this paper, integrates a synchronous context-free grammar (SCFG) and a synchronous tree sequence substitution grammar (STSSG) for statistical machine translation. The experimental results on NIST MT05 Chinese-to-English test set show that the SSG based translation system achieves significant improvement over three baseline systems.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Hongfei Jiang, Muyun Yang, Tiejun Zhao, Sheng Li, Bo Wang
• Keywords: ACL 2009, Computational Linguistics, Grammar, Synchronous Context-free Grammar, Synchronous Grammar