Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
Computation and Language
2018-05-22 v1
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.
Cite
@article{arxiv.1805.07469,
title = {Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations},
author = {Hiroki Shimanaka and Tomoyuki Kajiwara and Mamoru Komachi},
journal= {arXiv preprint arXiv:1805.07469},
year = {2018}
}
Comments
NAACL 2018 Student Research Workshop; 6 pages