@inproceedings{eriguchi-etal-2019-combining,
    title = "Combining Translation Memory with Neural Machine Translation",
    author = "Eriguchi, Akiko  and
      Rarrick, Spencer  and
      Matsushita, Hitokazu",
    editor = "Nakazawa, Toshiaki  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Kunchukuttan, Anoop  and
      Doi, Nobushige  and
      Oda, Yusuke  and
      Bojar, Ond{\v{r}}ej  and
      Parida, Shantipriya  and
      Goto, Isao  and
      Mino, Hidaya",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D19-5214/",
    doi = "10.18653/v1/D19-5214",
    pages = "123--130",
    abstract = "In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT system, when the similarity score of a test source sentence exceeds the predefined threshold. We observed that this combination approach significantly improves the translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. We also conducted source-based direct assessment on the final output, and we discuss the comparison between human references and each system`s output."
}