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LEPOR: A Robust Evaluation Metric for Machine Translation with Augmented Factors
Aaron L. F. HAN; Derek F. WONG; Lidia S. CHAO
2012
Conference NameProceedings of COLING 2012: Posters
Source PublicationCOLING 2012: Posters
Pages441–450
Conference DateDecember 2012.
Conference PlaceMumbai
Abstract

In the conventional evaluation metrics of machine translation, considering less information about the translations usually makes the result not reasonable and low correlation with human judgments. On the other hand, using many external linguistic resources and tools (e.g. Part-ofspeech tagging, morpheme, stemming, and synonyms) makes the metrics complicated, timeconsuming and not universal due to that different languages have the different linguistic features. This paper proposes a novel evaluation metric employing rich and augmented factors without relying on any additional resource or tool. Experiments show that this novel metric yields the state-of-the-art correlation with human judgments compared with classic metrics BLEU, TER, Meteor-1.3 and two latest metrics (AMBER and MP4IBM1), which proves it a robust one by employing a feature-rich and model-independent approach.

KeywordMachine Translation Recall Precision Modified Length Penalty Context-dependent N-gram Alignment Evaluation Metric
Language英语
Fulltext Access
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationNatural Language Processing & Portuguese-Chinese Machine Translation Laboratory Department of Computer and Information Science University of Macau Macau S.A.R., China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Aaron L. F. HAN,Derek F. WONG,Lidia S. CHAO. LEPOR: A Robust Evaluation Metric for Machine Translation with Augmented Factors[C],2012:441–450.
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