  The training data used in NMT is rarely controlled with respect to specific
attributes, such as word casing or gender, which can cause errors in
translations. We argue that predicting the target word and attributes
simultaneously is an effective way to ensure that translations are more
faithful to the training data distribution with respect to these attributes.
Experimental results on two tasks, uppercased input translation and gender
prediction, show that this strategy helps mirror the training data distribution
in testing. It also facilitates data augmentation on the task of uppercased
input translation.
