This paper describes Facebook FAIR's submission to the WMT19 shared news
translation task. We participate in two language pairs and four language
directions, English <-> German and English <-> Russian. Following our
submission from last year, our baseline systems are large BPE-based transformer
models trained with the Fairseq sequence modeling toolkit which rely on sampled
back-translations. This year we experiment with different bitext data filtering
schemes, as well as with adding filtered back-translated data. We also ensemble
and fine-tune our models on domain-specific data, then decode using noisy
channel model reranking. Our submissions are ranked first in all four
directions of the human evaluation campaign. On En->De, our system
significantly outperforms other systems as well as human translations. This
system improves upon our WMT'18 submission by 4.5 BLEU points.

论文介绍了 Facebook FAIR 参与 WMT19 共享新闻翻译任务的成果，使用大型 BPE-based transformer 模型以及 back-translation 技术，通过加入不同的双语数据过滤方案和领域特定数据的 fine-tune 以及 noisy channel reranking 等方式进行了实验，所有四个方向的提交均在人类评估活动中排名第一，在 En->De 方向上明显优于其他系统和人类翻译。