Paper: A Statistical Machine Translation Model Based on a Synthetic Synchronous Grammar

ACL ID P09-2032
Title A Statistical Machine Translation Model Based on a Synthetic Synchronous Grammar
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors

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 for- mal or linguistically motivated. Aim- ing at combining the strengths of differ- ent grammars, we describes a synthetic synchronous grammar (SSG), which ten- tatively in this paper, integrates a syn- chronous context-free grammar (SCFG) and a synchronous tree sequence substitu- tion grammar (STSSG) for statistical ma- chine translation. The experimental re- sults on NIST MT05 Chinese-to-English test set show that the SSG based transla- tion system achieves significant improve- ment over three baseline systems.