@InProceedings{shimanaka-kajiwara-komachi:2018:N18-4,
  author    = {Shimanaka, Hiroki  and  Kajiwara, Tomoyuki  and  Komachi, Mamoru},
  title     = {Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations},
  booktitle = {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},
  publisher = {Association for Computational Linguistics},
  pages     = {106--111},
  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 eval- uating the quality of machine translation. Al-though it is difficult to train sentence represen- tations using small-scale translation datasets with manual evaluation, sentence representa- tions 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.},
  url       = {http://www.aclweb.org/anthology/N18-4015}
}