@inproceedings{wang-etal-2020-tencent-ai,
    title = "Tencent {AI} Lab Machine Translation Systems for the {WMT}20 Biomedical Translation Task",
    author = "Wang, Xing  and
      Tu, Zhaopeng  and
      Wang, Longyue  and
      Shi, Shuming",
    editor = {Barrault, Lo{\"i}c  and
      Bojar, Ond{\v{r}}ej  and
      Bougares, Fethi  and
      Chatterjee, Rajen  and
      Costa-juss{\`a}, Marta R.  and
      Federmann, Christian  and
      Fishel, Mark  and
      Fraser, Alexander  and
      Graham, Yvette  and
      Guzman, Paco  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Martins, Andr{\'e}  and
      Morishita, Makoto  and
      Monz, Christof  and
      Nagata, Masaaki  and
      Nakazawa, Toshiaki  and
      Negri, Matteo},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.wmt-1.97/",
    pages = "881--886",
    abstract = "This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German{\ensuremath{<}}-{\ensuremath{>}}English, English{\ensuremath{<}}-{\ensuremath{>}}German, Chinese{\ensuremath{<}}-{\ensuremath{>}}English and English{\ensuremath{<}}-{\ensuremath{>}}Chinese. We implement our system with model ensemble technique on different transformer architectures (Deep, Hybrid, Big, Large Transformers). To enlarge the in-domain bilingual corpus, we use back-translation of monolingual in-domain data in the target language as additional in-domain training data. Our systems in German-{\ensuremath{>}}English and English-{\ensuremath{>}}German are ranked 1st and 3rd respectively according to the official evaluation results in terms of BLEU scores."
}