@inproceedings{shimanaka-etal-2018-metric,
    title = "Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations",
    author = "Shimanaka, Hiroki  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    editor = "Cordeiro, Silvio Ricardo  and
      Oraby, Shereen  and
      Pavalanathan, Umashanthi  and
      Rim, Kyeongmin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N18-4015/",
    doi = "10.18653/v1/N18-4015",
    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 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."
}