摘要
目前,大多数机器翻译自动评测方法都没有考虑在未匹配的词语中可能包含被忽略的信息。本文提出一种在参考译文和待评测译文之间自动搜索模糊匹配词对的方法,并给出相似度的计算方法。模糊匹配和计算相似度的整个过程将通过一个例子进行说明。实验表明,我们的方法能够较好地找到被忽略的、有意义的词对。更重要的是,通过引入模糊匹配,BLEU的性能得到显著的提高。模糊匹配可以用来提高其他机器翻译自动评测方法的性能。
Most current automatic metrics of machine translation evaluation do not consider that among unmatched words there may be neglected information. In this paper, we describe a strategy to find fuzzy-matched word pairs between reference and candidate translations automatically and propose an approach to compute the similarity. The whole process of finding fuzzy-matched word pairs and computing their similarity is demonstrated in detail. Experiments show that our method is capable of finding neglected meaningful word pairs fairly well. More importantly, the performance of BLEU is significantly improved by integrating fuzzy matching. Fuzzy matching is possible to be utilized to improve other automatic methods.
出处
《中文信息学报》
CSCD
北大核心
2005年第3期45-53,共9页
Journal of Chinese Information Processing
基金
国家 8 6 3计划资助项目 (2 0 0 1AA114 0 10 )
关键词
人工智能
机器翻译
机器翻译评测
模糊匹配
artificial intelligence
machine translation
machine translation evaluation
fuzzy matching
