Rationale-Enhanced Language Models are Better Continual Relation Learners

• URL: http://arxiv.org/abs/2310.06547v1
• Date: Tue, 10 Oct 2023 11:50:27 GMT
• Title: Rationale-Enhanced Language Models are Better Continual Relation Learners
• Authors: Weimin Xiong, Yifan Song, Peiyi Wang, Sujian Li
• Abstract summary: 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.
• Score: 29.311298089285753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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