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.

我们探讨了基于任务导向的对话系统的对话信念状态跟踪（DST）问题。通过以Prompt为基础进行少样本学习，我们证明了Prompt-based方法在DST的Few-shot学习中具有潜在的潜力，并提供了未来改进的方向。

面向任务型对话系统的基于提示的小样本学习方法在信念状态跟踪中的研究