Continual Meta-Learning (or MCL) on recurrent problems?

Is anyone aware of any paper/research on the topic of Few-shot + Continual Learning (e.g. Continual Meta-Learning or Meta-Continual Learning) applied to a recurrent neural network type of problem?

I have sequences generated from different tasks, and I want to learn to adapt to these tasks in only a few examples.