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.

对话状态跟踪（DST）是记录对话交互中用户查询和目标的任务，当前方法透明地决定槽值，而人类通常采用更为深思熟虑的方法，通过收集相关对话回合的信息，然后推理出适当的值。本文提出的 Chain-of-Thought-Explanation（CoTE）模型旨在确定槽值后，逐步创建详细的解释，从而提高槽值的准确性和可靠性。为了提高 CoTE 的推理能力，我们进一步使用自动改写构建了更流畅和高质量的解释，即 CoTE-refined 方法。在三个公认的 DST 基准（MultiWOZ 2.2、WoZ 2.0 和 M2M）上进行的实验证实了 CoTE 的显著有效性。此外，通过细致的细粒度分析，我们发现 CoTE 在长对话回合、用户回应和推理步骤有较为明显的益处。