Fuzzy Matching in Machine Translation Evaluation
Shouxun Lin · Zhongwen xinxi xuebao · 2005
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.