BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Precision and recall of machine translation
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Chinese lexical analysis using hierarchical hidden Markov model
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
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
Chinese word segmentation and statistical machine translation
ACM Transactions on Speech and Language Processing (TSLP)
(Meta-) evaluation of machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Optimizing Chinese word segmentation for machine translation performance
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
A character-based joint model for Chinese word segmentation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Character-level machine translation evaluation for languages with ambiguous word boundaries
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
An empirical study on word segmentation for chinese machine translation
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Substring-based machine translation
Machine Translation
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Word is usually adopted as the smallest unit in most tasks of Chinese language processing. However, for automatic evaluation of the quality of Chinese translation output when translating from other languages, either a word-level approach or a character-level approach is possible. So far, there has been no detailed study to compare the correlations of these two approaches with human assessment. In this paper, we compare word-level metrics with character-level metrics on the submitted output of English-to-Chinese translation systems in the IWSLT'08 CT-EC and NIST'08 EC tasks. Our experimental results reveal that character-level metrics correlate with human assessment better than word-level metrics. Our analysis suggests several key reasons behind this finding.