  Continual learning is crucial for dialog state tracking (DST) in dialog
systems, since requirements from users for new functionalities are often
encountered. However, most of existing continual learning methods for DST
require task identities during testing, which is a severe limit in real-world
applications. In this paper, we aim to address continual learning of DST in the
class-incremental scenario (namely the task identity is unknown in testing).
Inspired by the recently emerging prompt tuning method that performs well on
dialog systems, we propose to use the prompt pool method, where we maintain a
pool of key-value paired prompts and select prompts from the pool according to
the distance between the dialog history and the prompt keys. The proposed
method can automatically identify tasks and select appropriate prompts during
testing. We conduct experiments on Schema-Guided Dialog dataset (SGD) and
another dataset collected from a real-world dialog application. Experiment
results show that the prompt pool method achieves much higher joint goal
accuracy than the baseline. After combining with a rehearsal buffer, the model
performance can be further improved.
