This paper describes the Microsoft submission to the WMT2018 news translation
shared task. We participated in one language direction -- English-German. Our
system follows current best-practice and combines state-of-the-art models with
new data filtering (dual conditional cross-entropy filtering) and sentence
weighting methods. We trained fairly standard Transformer-big models with an
updated version of Edinburgh's training scheme for WMT2017 and experimented
with different filtering schemes for Paracrawl. According to automatic metrics
(BLEU) we reached the highest score for this subtask with a nearly 2 BLEU point
margin over the next strongest system. Based on human evaluation we ranked
first among constrained systems. We believe this is mostly caused by our data
filtering/weighting regime.

本文介绍了微软参加 WMT2018 新闻翻译共享任务的提交，对于英语到德语的一种语言方向中，基于最佳实践，采用了最先进的模型和新数据过滤和句子加权方法来训练在 Paracrawl 上实验结果表明，根据自动指标 (BLEU)，我们在此子任务中得分最高，比接下来最强大的系统高近 2 BLEU 分数，根据人工评估，我们在受限系统中排名第一，我们认为这主要是由于我们的数据过滤 / 加权组合的实施。