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

Naver Labs Europe's Systems for the WMT19 Machine Translation Robustness Task