  We share a French-English parallel corpus of Foursquare restaurant reviews
(https://europe.naverlabs.com/research/natural-language-processing/machine-translation-of-restaurant-reviews),
and define a new task to encourage research on Neural Machine Translation
robustness and domain adaptation, in a real-world scenario where better-quality
MT would be greatly beneficial. We discuss the challenges of such
user-generated content, and train good baseline models that build upon the
latest techniques for MT robustness. We also perform an extensive evaluation
(automatic and human) that shows significant improvements over existing online
systems. Finally, we propose task-specific metrics based on sentiment analysis
or translation accuracy of domain-specific polysemous words.
