Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous relations. To address the issue, we introduce rationale, i.e., the explanations of relation classification results generated by large language models (LLM), into CRE task. Specifically, we design the multi-task rationale tuning strategy to help the model learn current relations robustly. We also conduct contrastive rationale replay to further distinguish analogous relations. Experimental results on two standard benchmarks demonstrate that our method outperforms the state-of-the-art CRE models.

翻译：持续关系抽取（CRE）旨在解决学习一系列新出现关系时的灾难性遗忘问题。近期CRE研究发现，灾难性遗忘源于模型对未来相似关系缺乏鲁棒性。为解决该问题，我们将解释（即大语言模型生成的关系分类结果的推理依据）引入CRE任务。具体而言，我们设计了多任务解释微调策略，以帮助模型稳健地学习当前关系；同时采用对比解释回放方法进一步区分相似关系。在两个标准基准上的实验结果表明，我们的方法优于当前最优的CRE模型。