  Leveraging shared learning through Massively Multilingual Models,
state-of-the-art machine translation models are often able to adapt to the
paucity of data for low-resource languages. However, this performance comes at
the cost of significantly bloated models which are not practically deployable.
Knowledge Distillation is one popular technique to develop competitive,
lightweight models: In this work, we first evaluate its use to compress MT
models focusing on languages with extremely limited training data. Through our
analysis across 8 languages, we find that the variance in the performance of
the distilled models due to their dependence on priors including the amount of
synthetic data used for distillation, the student architecture, training
hyperparameters and confidence of the teacher models, makes distillation a
brittle compression mechanism. To mitigate this, we explore the use of
post-training quantization for the compression of these models. Here, we find
that while distillation provides gains across some low-resource languages,
quantization provides more consistent performance trends for the entire range
of languages, especially the lowest-resource languages in our target set.
