MAXSIM: A Maximum Similarity Metric for Machine Translation Evaluation

We propose an automatic machine translation (MT) evaluation metric that calculates a similarity score (based on precision and recall) of a pair of sentences. Unlike most metrics, we compute a similarity score between items across the two sentences. We then find a maximum weight matching between the items such that each item in one sentence is mapped to at most one item in the other sentence. This general framework allows us to use arbitrary similarity functions between items, and to incorporate different information in our comparison, such as n-grams, dependency relations, etc. When evaluated on data from the ACL-07 MT workshop, our proposed metric achieves higher correlation with human judgements than all 11 automatic MT evaluation metrics that were evaluated during the workshop.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Yee Seng Chan, Hwee Tou Ng
• Keywords: ACL 2008, Arbitrary Similarity Functions, Computational Linguistics, Evaluation Metrics, Similarity Score