  Existing continual relation learning (CRL) methods rely on plenty of labeled
training data for learning a new task, which can be hard to acquire in real
scenario as getting large and representative labeled data is often expensive
and time-consuming. It is therefore necessary for the model to learn novel
relational patterns with very few labeled data while avoiding catastrophic
forgetting of previous task knowledge. In this paper, we formulate this
challenging yet practical problem as continual few-shot relation learning
(CFRL). Based on the finding that learning for new emerging few-shot tasks
often results in feature distributions that are incompatible with previous
tasks' learned distributions, we propose a novel method based on embedding
space regularization and data augmentation. Our method generalizes to new
few-shot tasks and avoids catastrophic forgetting of previous tasks by
enforcing extra constraints on the relational embeddings and by adding extra
{relevant} data in a self-supervised manner. With extensive experiments we
demonstrate that our method can significantly outperform previous
state-of-the-art methods in CFRL task settings.
