Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time consuming and costly. However, DST extends beyond simple slot-filling and requires effective updating strategies for tracking dialogue state as conversations progress. In this paper, we propose ParsingDST, a new In-Context Learning (ICL) method, to introduce additional intricate updating strategies in zero-shot DST. Our approach reformulates the DST task by leveraging powerful Large Language Models (LLMs) and translating the original dialogue text to JSON through semantic parsing as an intermediate state. We also design a novel framework that includes more modules to ensure the effectiveness of updating strategies in the text-to-JSON process. Experimental results demonstrate that our approach outperforms existing zero-shot DST methods on MultiWOZ, exhibiting significant improvements in Joint Goal Accuracy (JGA) and slot accuracy compared to existing ICL methods.

翻译：零样本对话状态跟踪（DST）解决了任务导向型对话获取与标注的挑战，该过程通常耗时且成本高昂。然而，DST并非简单的槽位填充，它需要有效的更新策略来跟踪对话进程中的状态演变。本文提出ParsingDST——一种新型上下文学习（ICL）方法——为零样本DST引入额外复杂更新策略。我们的方法通过利用强大大型语言模型（LLM）重构DST任务，并借助语义解析将原始对话文本转换为JSON中间状态。同时，我们设计包含更多模块的创新框架，确保文本到JSON转换过程中更新策略的有效性。实验结果表明，我们的方法在MultiWOZ数据集上优于现有零样本DST方法，与现有ICL方法相比，在联合目标准确率（JGA）和槽位准确率方面均表现出显著提升。