Representation based translation evaluation metrics

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.3115/v1/P15-2025
AuthorSearch for: 1; Search for: 1
Name affiliation
  1. National Research Council of Canada. Information and Communication Technologies
FormatText, Article
Proceedings titleProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7the International Joint Conference on Natural Language Processing (Short Papers), Beijing, China, July 26-31, 2015
ConferenceThe 53rd Annual Meeting of the Association for Computational Linguistics and the 7the International Joint Conference on Natural Language Processing, July 26-31, 2015, Beijing, China
ISBN9781510808461
1510808469
Pages150–155
Abstract
Precisely evaluating the quality of a translation against human references is a challenging task due to the flexible word ordering of a sentence and the existence of a large number of synonyms for words. This paper proposes to evaluate translations with distributed representations of words and sentences. We study several metrics based on word and sentence representations and their combination. Experiments on the WMT metric task shows that the metric based on the combined representations achieves the best performance, outperforming the state-of-the-art translation metrics by a large margin. In particular , training the distributed representations only needs a reasonable amount of mono-lingual, unlabeled data that is not necessary drawn from the test domain.
Publication date
PublisherAssociation for Computational Linguistics
LanguageEnglish
Peer reviewedYes
NPARC number23001598
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Record created2017-03-09
Record modified2020-04-22
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