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 both within-task and cross-task variations. 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.

翻译：基于记忆的方法在持续关系抽取（CRE）中已展现出强劲性能。然而，存储先前任务的样本会增加内存使用并引发隐私担忧。近年来，基于提示的方法作为一种有前景的替代方案出现，因其不依赖于存储历史样本。尽管取得了这些进展，当前基于提示的技术在CRE中仍面临若干核心挑战，特别是在准确识别任务身份和缓解灾难性遗忘方面。现有的提示选择策略常存在不准确的问题，缺乏防止共享参数遗忘的鲁棒机制，并且难以同时处理跨任务与任务内的变化。本文提出WAVE++，一种受前缀调优与专家混合模型之间联系启发的新方法。具体而言，我们引入了任务特定的提示池，以增强跨不同任务的灵活性和适应性，同时避免跨边界风险；该设计能更有效地捕获任务内与跨任务的变化。为进一步优化关系分类，我们整合了标签描述以提供更丰富、更全局的上下文，使模型能更好地区分不同关系。我们还提出了一种免训练机制以改进推理期间的任务预测。此外，我们集成生成模型以在共享参数中巩固先验知识，从而无需显式数据存储。大量实验表明，WAVE++在性能上超越了最先进的基于提示和基于复现的方法，为持续关系抽取提供了更鲁棒的解决方案。我们的代码公开于 https://github.com/PiDinosauR2804/WAVE-CRE-PLUS-PLUS。