论文标题
通过结构化提示进行连续的质量检查
Continuous QA Learning with Structured Prompts
论文作者
论文摘要
具有终身学习能力（LL）能力的质量检查模型对于实用质量检查应用很重要，据报道，基于架构的LL方法是这些模型的有效实现。但是，将以前的方法扩展到质量检查任务是不平凡的，因为它们要么需要在测试阶段访问任务身份，要么不会从看不见的任务中明确对样本进行建模。在本文中，我们提出了Diana：一种基于动态体系结构的终生质量检查模型，该模型试图通过迅速增强的语言模型来学习一系列QA任务。戴安娜（Diana）使用四种类型的分层组织提示来捕获来自不同粒度的质量检查知识。具体来说，我们专门介绍任务级的提示来捕获特定任务的知识，以保留高LL表现并维护实例级别的提示，以学习跨不同输入样本共享的知识，以提高模型的概括性能。此外，我们专门介绍单独的提示，以明确地对未看到的任务进行建模，并引入一组及时的密钥向量，以促进任务之间的知识共享。广泛的实验表明，戴安娜（Diana）的表现优于最先进的终身质量检查模型，尤其是在处理看不见的任务时。
QA models with lifelong learning (LL) abilities are important for practical QA applications, and architecture-based LL methods are reported to be an effective implementation for these models. However, it is non-trivial to extend previous approaches to QA tasks since they either require access to task identities in the testing phase or do not explicitly model samples from unseen tasks. In this paper, we propose Diana: a dynamic architecture-based lifelong QA model that tries to learn a sequence of QA tasks with a prompt enhanced language model. Four types of hierarchically organized prompts are used in Diana to capture QA knowledge from different granularities. Specifically, we dedicate task-level prompts to capture task-specific knowledge to retain high LL performances and maintain instance-level prompts to learn knowledge shared across different input samples to improve the model's generalization performance. Moreover, we dedicate separate prompts to explicitly model unseen tasks and introduce a set of prompt key vectors to facilitate knowledge sharing between tasks. Extensive experiments demonstrate that Diana outperforms state-of-the-art lifelong QA models, especially in handling unseen tasks.
