  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.
