  Dialogue state tracking (DST) module is an important component for
task-oriented dialog systems to understand users' goals and needs. Collecting
dialogue state labels including slots and values can be costly, especially with
the wide application of dialogue systems in more and more new-rising domains.
In this paper, we focus on how to utilize the language understanding and
generation ability of pre-trained language models for DST. We design a dual
prompt learning framework for few-shot DST. Specifically, we consider the
learning of slot generation and value generation as dual tasks, and two prompts
are designed based on such a dual structure to incorporate task-related
knowledge of these two tasks respectively. In this way, the DST task can be
formulated as a language modeling task efficiently under few-shot settings.
Experimental results on two task-oriented dialogue datasets show that the
proposed method not only outperforms existing state-of-the-art few-shot
methods, but also can generate unseen slots. It indicates that DST-related
knowledge can be probed from PLM and utilized to address low-resource DST
efficiently with the help of prompt learning.
