Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction achieved by maintaining a predefined 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：对话状态跟踪（DST）旨在记录交互过程中用户的查询和目标，通过维护一组预定义的槽位及其对应值来实现。现有方法通常以不透明的方式决定槽位值，而人类更倾向于采用深思熟虑的方法，从相关对话轮次中收集信息，然后推理出合适的值。本文聚焦于推理槽位值所需的步骤，提出一种名为链式思维解释（CoTE）的模型用于DST任务。CoTE建立在生成式DST框架之上，其设计目标是在确定槽位值后逐步生成详细解释。这一过程能够得出更准确、更可靠的槽位值。此外，为进一步提升CoTE的推理能力，我们通过自动释义构建更流畅、更高质量的解释，从而得到改进版方法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，我们观察到在对话轮次长、用户响应长且推理步骤多的样本上，CoTE带来了显著的性能提升。