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

A Study on Prompt-based Few-Shot Learning Methods for Belief State Tracking in Task-oriented Dialog Systems