多策略机器翻译系统IHSMTS中实例模式泛化匹配算法  

Generalizing Match of Translation Examples in IHSMTS

作　　者：张孝飞[1] 陈肇雄[1] 黄河燕[1] 胡春玲[1] 

机构地区：[1]中国科学院计算机语言信息工程研究中心,北京100083

出　　处：《中文信息学报》2005年第4期1-9,共9页Journal of Chinese Information Processing

基　　金：国家自然科学基金资助项目(6027088);国家863计划资助项目(2002AA117010-02)

摘　　要：基于精确匹配的EBMT,由于翻译覆盖率过低,导致其难以大规模实际应用。本文提出一种实例模式泛化匹配算法,试图改善EBMT的翻译覆盖率:以输入的待翻译句子为目标导向,对候选翻译实例有针对性地进行实时泛化,使得算法既能满足实时文档翻译对速度的要求,又能充分利用系统使用过程中用户新添加和修改的翻译知识,从而总体上提高了系统的翻译覆盖率和翻译质量。实验结果表明,在语料规模为16万句对的情况下,系统翻译覆盖率达到了75%左右,充分说明了本文算法的有效性。Example-based machine translation is currently difficult in large-scale implications because of its low translation coverage. In this paper, an algorithm of generalizing match of translation examples is proposed to improve the translation coverage of EBMT: the candidate translation examples are generalized in real time controlled and guided by the input sentence which to be translated. The algorithm not only can satisfy the speed of real time documents translation but also can use the new language knowledge which added and revised by users in the translation processing. So a higher translation coverage and translation quality is obtained as a whole. The positive experiment results of 75% translation coverage basis on 160,000 pairs of translation examples confirm the algorithm’s effect.

关 键 词：人工智能 机器翻译 基于实例的机器翻译 泛化匹配 翻译覆盖率 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]