BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
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Machine Translation
(Meta-) evaluation of machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Linguistic features for automatic evaluation of heterogenous MT systems
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Further meta-evaluation of machine translation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Findings of the 2009 workshop on statistical machine translation
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Evaluating machine translations using mNCD
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
TESLA at WMT 2011: translation evaluation and tunable metric
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Better evaluation metrics lead to better machine translation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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This paper evaluates the performance of our recently proposed automatic machine translation evaluation metric MaxSim and examines the impact of translation fluency on the metric. MaxSim calculates a similarity score between a pair of English system-reference sentences by comparing information items such as n-grams across the sentence pair. Unlike most metrics which perform binary matching, MaxSim also computes similarity scores between items and models them as nodes in a bipartite graph to select a maximum weight matching. Our experiments show that MaxSim is competitive with state-of-the-art metrics on benchmark datasets.