  As a commercial provider of machine translation, we are constantly training
engines for a variety of uses, languages, and content types. In each case,
there can be many variables, such as the amount of training data available, and
the quality requirements of the end user. These variables can have an impact on
the robustness of Neural MT engines. On the whole, Neural MT cures many ills of
other MT paradigms, but at the same time, it has introduced a new set of
challenges to address. In this paper, we describe some of the specific issues
with practical NMT and the approaches we take to improve model robustness in
real-world scenarios.
