The recent success of deep learning for NLP tasks is at least partially due to the fact that such models are exposed to large quantities of labeled data.
with MT, we can "plug in" any task simply by generating a different input vector X (and, potentially, a different scheme to insert noise), whereas LN requires an adaptation of the entire architecture due to the tight connections between teacher and student.