Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation

Understand

Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream.

• CRE model usually suffers from catastrophic forgetting problem, i.e., the performance of old relations seriously degrades when the model learns new relations.
• Most previous work attributes catastrophic forgetting to the corruption of the learned representations as new relations come, with an implicit assumption that the CRE models have adequately learned the old relations.
• In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process.