Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41975
Title: MAXSIM: A maximum similarity metric for machine translation evaluation
Authors: Chan, Y.S. 
Ng, H.T. 
Issue Date: 2008
Source: Chan, Y.S.,Ng, H.T. (2008). MAXSIM: A maximum similarity metric for machine translation evaluation. ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference : 55-62. ScholarBank@NUS Repository.
Abstract: 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. © 2008 Association for Computational Linguistics.
Source Title: ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/41975
ISBN: 9781932432046
Appears in Collections:Staff Publications

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