English

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.

Keywords

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

R2 v1 2026-06-23T02:00:48.253Z