Continual Generalized Intent Discovery: Marching Towards Dynamic and
  Open-world Intent Recognition
Xiaoshuai Song Yutao Mou Keqing He Yueyan Qiu Pei Wang Weiran Xu
In a practical dialogue system, users may input out-of-domain (OOD) queries.
The Generalized Intent Discovery (GID) task aims to discover OOD intents from
OOD queries and extend them to the in-domain (IND) classifier. However, GID
only considers one stage of OOD learning, and needs to utilize the data in all
previous stages for joint training, which limits its wide application in
reality. In this paper, we introduce a new task, Continual Generalized Intent
Discovery (CGID), which aims to continuously and automatically discover OOD
intents from dynamic OOD data streams and then incrementally add them to the
classifier with almost no previous data, thus moving towards dynamic intent
recognition in an open world. Next, we propose a method called Prototype-guided
Learning with Replay and Distillation (PLRD) for CGID, which bootstraps new
intent discovery through class prototypes and balances new and old intents
through data replay and feature distillation. Finally, we conduct detailed
experiments and analysis to verify the effectiveness of PLRD and understand the
key challenges of CGID for future research.