Generalized Few-Shot Intent Detection (GFSID) is challenging and realistic because it needs to categorize both seen and novel intents simultaneously. Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents. To address the dilemma, we propose to convert the GFSID task into the class incremental learning paradigm. Specifically, we propose a two-stage learning framework, which sequentially learns the knowledge of different intents in various periods via prompt learning. And then we exploit prototypes for categorizing both seen and novel intents. Furthermore, to achieve the transfer knowledge of intents in different stages, for different scenarios we design two knowledge preservation methods which close to realistic applications. Extensive experiments and detailed analyses on two widely used datasets show that our framework based on the class incremental learning paradigm achieves promising performance.

翻译：广义少样本文本意图检测（GFSID）任务具有挑战性和现实意义，因其需同时分类已知和新出现意图。现有GFSID方法依赖情节学习范式，难以扩展到广义场景，因其未显式学习已知类别的分类与已知意图知识。为解决这一困境，我们提出将GFSID任务转化为类增量学习范式。具体而言，我们构建两阶段学习框架，通过提示学习在不同阶段顺序学习不同意图的知识；随后利用原型对已知和新意图进行分类。此外，为实现跨阶段意图知识迁移，针对不同场景设计了两种接近实际应用的知识保留方法。在两个广泛使用的数据集上的大量实验和详细分析表明，基于类增量学习范式的框架取得了令人满意的表现。