@inproceedings{bei-etal-2020-gtcom,
    title = "{GTCOM} Neural Machine Translation Systems for {WMT}20",
    author = "Bei, Chao  and
      Zong, Hao  and
      Liu, Qingmin  and
      Yuan, Conghu",
    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/fix-sig-urls/2020.wmt-1.6/",
    pages = "100--104",
    abstract = "This paper describes the Global Tone Communication Co., Ltd.{'}s submission of the WMT20 shared news translation task. We participate in four directions: English to (Khmer and Pashto) and (Khmer and Pashto) to English. Further, we get the best BLEU scores in the directions of English to Pashto, Pashto to English and Khmer to English (13.1, 23.1 and 25.5 respectively) among all the participants. Our submitted systems are unconstrained and focus on mBART (Multilingual Bidirectional and Auto-Regressive Transformers), back-translation and forward-translation. Also, we apply rules, language model and RoBERTa model to filter monolingual, parallel sentences and synthetic sentences. Besides, we validate the difference of the vocabulary built from monolingual data and parallel data."
}