  We introduce a curriculum learning approach to adapt generic neural machine
translation models to a specific domain. Samples are grouped by their
similarities to the domain of interest and each group is fed to the training
algorithm with a particular schedule. This approach is simple to implement on
top of any neural framework or architecture, and consistently outperforms both
unadapted and adapted baselines in experiments with two distinct domains and
two language pairs.
