  Real-life multilingual systems should be able to efficiently incorporate new
languages as data distributions fed to the system evolve and shift over time.
To do this, systems need to handle the issue of catastrophic forgetting, where
the model performance drops for languages or tasks seen further in its past. In
this paper, we study catastrophic forgetting, as well as methods to minimize
this, in a massively multilingual continual learning framework involving up to
51 languages and covering both classification and sequence labeling tasks. We
present LR ADJUST, a learning rate scheduling method that is simple, yet
effective in preserving new information without strongly overwriting past
knowledge. Furthermore, we show that this method is effective across multiple
continual learning approaches. Finally, we provide further insights into the
dynamics of catastrophic forgetting in this massively multilingual setup.
