Efficient Lifelong Relation Extraction with Dynamic Regularization

Abstract

Relation extraction has received increasing attention due to its important role in natural language processing applications. However, most existing methods are designed for a fixed set of relations. They are unable to handle the lifelong learning scenario, i.e. adapting a well-trained model to newly added relations without catastrophically forgetting the previously learned knowledge. In this work, we present a memory-efficient dynamic regularization method to address this issue. Specifically, two types of powerful consolidation regularizers are applied to preserve the learned knowledge and ensure the robustness of the model, and the regularization strength is adaptively adjusted with respect to the dynamics of the training losses. Experiment results on multiple benchmarks show that our proposed method significantly outperforms prior state-of-the-art approaches.