@inproceedings{peng-etal-2019-huaweis,
    title = "Huawei`s {NMT} Systems for the {WMT} 2019 Biomedical Translation Task",
    author = "Peng, Wei  and
      Liu, Jianfeng  and
      Li, Liangyou  and
      Liu, Qun",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Martins, Andr{\'e}  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Turchi, Marco  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-5420/",
    doi = "10.18653/v1/W19-5420",
    pages = "164--168",
    abstract = "This paper describes Huawei`s neural machine translation systems for the WMT 2019 biomedical translation shared task. We trained and fine-tuned our systems on a combination of out-of-domain and in-domain parallel corpora for six translation directions covering English{--}Chinese, English{--}French and English{--}German language pairs. Our submitted systems achieve the best BLEU scores on English{--}French and English{--}German language pairs according to the official evaluation results. In the English{--}Chinese translation task, our systems are in the second place. The enhanced performance is attributed to more in-domain training and more sophisticated models developed. Development of translation models and transfer learning (or domain adaptation) methods has significantly contributed to the progress of the task."
}