  Knowledge distillation (KD) is a well-known method for compressing neural
models. However, works focusing on distilling knowledge from large multilingual
neural machine translation (MNMT) models into smaller ones are practically
nonexistent, despite the popularity and superiority of MNMT. This paper bridges
this gap by presenting an empirical investigation of knowledge distillation for
compressing MNMT models. We take Indic to English translation as a case study
and demonstrate that commonly used language-agnostic and language-aware KD
approaches yield models that are 4-5x smaller but also suffer from performance
drops of up to 3.5 BLEU. To mitigate this, we then experiment with design
considerations such as shallower versus deeper models, heavy parameter sharing,
multi-stage training, and adapters. We observe that deeper compact models tend
to be as good as shallower non-compact ones, and that fine-tuning a distilled
model on a High-Quality subset slightly boosts translation quality. Overall, we
conclude that compressing MNMT models via KD is challenging, indicating immense
scope for further research.
