A CASCADED APPROACH TO THE OPTIMIZATION OF TRANSLATION RULES

摘要：

As far as the rule-based machine translation (RBMT) is concerned, the rule acquisition remains as a bottle-neck problem. This paper proposes a cascaded approach to optimize the rule base, which is automatically acquired from the bilingual corpus. Observing the more risk of errors in the upper layer of the parsing tree, we propose in this paper a method which advocates the optimization of rules by a bottom-up strategy so as to take the advantage of correctness of parsing results near the leaf nodes. The experimental results further prove that such cascaded optimization out-performs the usual practice.

关键词： Machine Translation Rule Optimization Syntactic Tree

作者: SHU-JIE LIU MU-YUN YANG TIE-JUN ZHAO

作者单位: MOE-MS Key Laboratory of NLP and Speech, Harbin Institute of Technology, Harbin, 150001, China MOE-MS Key Laboratory of NLP and Speech, Harbin Institute of Technology, Harbin, 150001, China;Schoo

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 4089-4092

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）