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）解决了任务型对话获取和标注耗时且成本高昂的挑战。然而，DST不仅限于简单的槽位填充，还需要有效的更新策略来跟踪对话状态随对话进程的变化。本文提出了一种新的上下文学习（ICL）方法ParsingDST，以在零样本DST中引入额外的复杂更新策略。我们的方法通过利用强大的大语言模型（LLM）重构DST任务，并以语义解析为中间状态将原始对话文本转化为JSON格式。我们还设计了一个新颖的框架，包含更多模块以确保文本到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）和槽位准确率上均有显著提升。