Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations

Hiroki Shimanaka, Tomoyuki Kajiwara, Mamoru Komachi


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. Al-though 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.
Anthology ID:
N18-4015
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
June
Year:
2018
Address:
New Orleans, Louisiana, USA
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–111
Language:
URL:
https://www.aclweb.org/anthology/N18-4015
DOI:
10.18653/v1/N18-4015
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-4015.pdf