Generalized few-shot intent detection by prompt learning without forgetting
摘要
Generalized Few-Shot Intent Detection (GFSID) is a challenging and realistic task as it requires to categorize both seen and novel intents simultaneously. Previous methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup. Furthermore, they ignore preserving learned knowledge. To address the dilemma, we propose to convert the GFSID task into the class incremental learning paradigm. Specifically, we propose a two-phase learning framework based on prompt learning, which sequentially learns the knowledge of different intents in various periods. To retain learned knowledge we introduce two knowledge preservation methods for distinct real-life scenarios. Extensive experiments and detailed analyses on two widely used datasets show that our framework based on the class incremental learning paradigm achieves promising performance.