  We tackle the Dialogue Belief State Tracking(DST) problem of task-oriented
conversational systems. Recent approaches to this problem leveraging
Transformer-based models have yielded great results. However, training these
models is expensive, both in terms of computational resources and time.
Additionally, collecting high quality annotated dialogue datasets remains a
challenge for researchers because of the extensive annotation required for
training these models. Driven by the recent success of pre-trained language
models and prompt-based learning, we explore prompt-based few-shot learning for
Dialogue Belief State Tracking. We formulate the DST problem as a 2-stage
prompt-based language modelling task and train language models for both tasks
and present a comprehensive empirical analysis of their separate and joint
performance. We demonstrate the potential of prompt-based methods in few-shot
learning for DST and provide directions for future improvement.
