  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.
