  This paper describes the systems that we submitted to the WMT19 Machine
Translation robustness task. This task aims to improve MT's robustness to noise
found on social media, like informal language, spelling mistakes and other
orthographic variations. The organizers provide parallel data extracted from a
social media website in two language pairs: French-English and Japanese-English
(in both translation directions). The goal is to obtain the best scores on
unseen test sets from the same source, according to automatic metrics (BLEU)
and human evaluation. We proposed one single and one ensemble system for each
translation direction. Our ensemble models ranked first in all language pairs,
according to BLEU evaluation. We discuss the pre-processing choices that we
made, and present our solutions for robustness to noise and domain adaptation.
