论文标题
蒂尔德（WMT）2020：新闻任务系统
Tilde at WMT 2020: News Task Systems
论文作者
论文摘要
本文介绍了Tilde对WMT2020在新闻翻译上的共享任务的提交，该任务在受约束和无约束的曲目中的英语语言对的两个方向上都提交了。我们遵循前几年的提交，并构建基线系统，以形态学动机，基于次字母单位的变压器基本模型，我们使用Marian Machine Translation Toolkit训练。此外，我们尝试了不同的平行和单语数据选择方案，并进行了反翻译。我们的最终模型是变压器基础和变压器大型模型的合奏，这些模型具有直至左的重新排行榜。
This paper describes Tilde's submission to the WMT2020 shared task on news translation for both directions of the English-Polish language pair in both the constrained and the unconstrained tracks. We follow our submissions from the previous years and build our baseline systems to be morphologically motivated sub-word unit-based Transformer base models that we train using the Marian machine translation toolkit. Additionally, we experiment with different parallel and monolingual data selection schemes, as well as sampled back-translation. Our final models are ensembles of Transformer base and Transformer big models that feature right-to-left re-ranking.
