  Sparsely gated Mixture of Experts (MoE) models have been shown to be a
compute-efficient method to scale model capacity for multilingual machine
translation. However, for low-resource tasks, MoE models severely over-fit. We
show effective regularization strategies, namely dropout techniques for MoE
layers in EOM and FOM, Conditional MoE Routing and Curriculum Learning methods
that prevent over-fitting and improve the performance of MoE models on
low-resource tasks without adversely affecting high-resource tasks. On a
massively multilingual machine translation benchmark, our strategies result in
about +1 chrF++ improvement in very low resource language pairs. We perform an
extensive analysis of the learned MoE routing to better understand the impact
of our regularization methods and how we can improve them.
