Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
May 18, 2018 ยท Declared Dead ยท ๐ North American Chapter of the Association for Computational Linguistics
"No code URL or promise found in abstract"
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Authors
Hiroki Shimanaka, Tomoyuki Kajiwara, Mamoru Komachi
arXiv ID
1805.07469
Category
cs.CL: Computation & Language
Citations
6
Venue
North American Chapter of the Association for Computational Linguistics
Last Checked
4 months ago
Abstract
Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of universal sentence representations for evaluating the quality of machine translation. Although it is difficult to train sentence representations using small-scale translation datasets with manual evaluation, sentence representations trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. Experimental results of the WMT-2016 dataset show that the proposed method achieves state-of-the-art performance with sentence representation features only.
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