An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction
Shen Zhou, Yongqi Li, Xin Miao, Tieyun Qian · 2024
Continual relation extraction (CRE) aims to continuously learn relations in new tasks without forgetting old relations in previous tasks.Current CRE methods are all rehearsal-based, which need to store samples and thus may encounter privacy and security issues.This paper targets rehearsal-free continual relation extraction for the first time and decomposes it into task identification and within-task prediction sub-problems.Existing rehearsal-free methods focus on training a model (expert) for withintask prediction yet neglect to enhance the models' capability of task identification.In this paper, we propose an Ensemble-of-Experts (EoE) framework for rehearsal-free continual relation extraction.Specifically, we first discriminatively train each expert by augmenting analogous relations across tasks to enhance the expert's task identification ability.We then propose a cascade voting mechanism to form an ensemble of experts for effectively aggregating their abilities.Extensive experiments show that our method outperforms current rehearsal-free methods and is even better than rehearsal-based CRE methods.