Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction achieved by maintaining a prede- fined set of slots and their corresponding values. Current approaches decide slot values opaquely, while humans usually adopt a more deliberate approach by collecting information from relevant dialogue turns and then reasoning the appropriate values. In this work, we focus on the steps needed to figure out slot values by proposing a model named Chain-of-Thought-Explanation (CoTE) for the DST task. CoTE, which is built on the generative DST framework, is designed to create detailed explanations step by step after determining the slot values. This process leads to more accurate and reliable slot values. More-over, to improve the reasoning ability of the CoTE, we further construct more fluent and high-quality explanations with automatic paraphrasing, leading the method CoTE-refined. Experimental results on three widely recognized DST benchmarks-MultiWOZ 2.2, WoZ 2.0, and M2M-demonstrate the remarkable effectiveness of the CoTE. Furthermore, through a meticulous fine-grained analysis, we observe significant benefits of our CoTE on samples characterized by longer dialogue turns, user responses, and reasoning steps.

翻译：对话状态追踪（DST）旨在通过维护预定义的槽位及其对应值，记录对话交互过程中用户的查询与目标。现有方法以不透明的方式决定槽位值，而人类通常采用更审慎的方式：从相关对话轮次中收集信息，再推理出合适的值。本文聚焦于推理槽位值所需的步骤，提出名为“思维链解释”（Chain-of-Thought-Explanation, CoTE）的DST任务模型。CoTE基于生成式DST框架，在确定槽位值后逐步生成详细解释，从而获得更准确可靠的槽位值。此外，为提升CoTE的推理能力，我们通过自动释义构建更流畅、高质量的解释，提出了改进方法CoTE-refined。在三个广泛认可的DST基准数据集——MultiWOZ 2.2、WoZ 2.0和M2M上的实验结果表明，CoTE具有显著有效性。进一步通过细致的细粒度分析，我们观察到CoTE在具有较长对话轮次、用户响应和推理步骤的样本上具有显著优势。