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.

翻译：对话状态跟踪模块是任务导向型对话系统中理解用户目标和需求的重要组成部分。收集包含槽位和值的对话状态标签成本高昂，尤其是在对话系统广泛应用于越来越多新兴领域的背景下。本文聚焦如何利用预训练语言模型的语言理解与生成能力进行对话状态跟踪。我们设计了一种双提示学习框架用于少样本对话状态跟踪。具体而言，我们将槽位生成与值生成的学习视为双重任务，并基于这种对偶结构设计两个提示，分别融入这两个任务的相关知识。通过这种方式，在少样本设置下，对话状态跟踪任务可高效转化为语言建模任务。在两个任务导向型对话数据集上的实验结果表明，所提方法不仅优于现有最先进的少样本方法，还能生成未见的槽位。这表明，借助提示学习，可从预训练语言模型中探测对话状态跟踪相关知识，并有效利用其应对低资源对话状态跟踪问题。