  Continual relation extraction (CRE) aims to extract relations towards the
continuous and iterative arrival of new data, of which the major challenge is
the catastrophic forgetting of old tasks. In order to alleviate this critical
problem for enhanced CRE performance, we propose a novel Continual Relation
Extraction framework with Contrastive Learning, namely CRECL, which is built
with a classification network and a prototypical contrastive network to achieve
the incremental-class learning of CRE. Specifically, in the contrastive network
a given instance is contrasted with the prototype of each candidate relations
stored in the memory module. Such contrastive learning scheme ensures the data
distributions of all tasks more distinguishable, so as to alleviate the
catastrophic forgetting further. Our experiment results not only demonstrate
our CRECL's advantage over the state-of-the-art baselines on two public
datasets, but also verify the effectiveness of CRECL's contrastive learning on
improving CRE performance.
