  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.
