Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive Prompting

• URL: http://arxiv.org/abs/2505.13944v1
• Date: Tue, 20 May 2025 05:22:17 GMT
• Title: Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive Prompting
• Authors: Bao-Ngoc Dao, Quang Nguyen, Luyen Ngo Dinh, Minh Le, Nam Le, Linh Ngo Van,
• Abstract summary: WAVE++ is a novel approach inspired by the connection between prefix-tuning and mixture of experts.<n>We introduce task-specific prompt pools that enhance flexibility and adaptability across diverse tasks.<n>We incorporate label descriptions that provide richer, more global context, enabling the model to better distinguish among different relations.
• Score: 2.818102173042532
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory-based approaches have shown strong performance in Continual Relation Extraction (CRE). However, storing examples from previous tasks increases memory usage and raises privacy concerns. Recently, prompt-based methods have emerged as a promising alternative, as they do not rely on storing past samples. Despite this progress, current prompt-based techniques face several core challenges in CRE, particularly in accurately identifying task identities and mitigating catastrophic forgetting. Existing prompt selection strategies often suffer from inaccuracies, lack robust mechanisms to prevent forgetting in shared parameters, and struggle to handle both cross-task and within-task variations. In this paper, we propose WAVE++, a novel approach inspired by the connection between prefix-tuning and mixture of experts. Specifically, we introduce task-specific prompt pools that enhance flexibility and adaptability across diverse tasks while avoiding boundary-spanning risks; this design more effectively captures variations within each task and across tasks. To further refine relation classification, we incorporate label descriptions that provide richer, more global context, enabling the model to better distinguish among different relations. We also propose a training-free mechanism to improve task prediction during inference. Moreover, we integrate a generative model to consolidate prior knowledge within the shared parameters, thereby removing the need for explicit data storage. Extensive experiments demonstrate that WAVE++ outperforms state-of-the-art prompt-based and rehearsal-based methods, offering a more robust solution for continual relation extraction. Our code is publicly available at https://github.com/PiDinosauR2804/WAVE-CRE-PLUS-PLUS.

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