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Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiringand annotating task-oriented dialogues, which can be time-consuming and costly.However, DST extends beyond simple slot-filling and requires effective updatingstrategies for tracking dialogue state as conversations progress. In thispaper, we propose ParsingDST, a new In-Context Learning (ICL) method, tointroduce additional intricate updating strategies in zero-shot DST. Ourapproach reformulates the DST task by leveraging powerful Large Language Models(LLMs) and translating the original dialogue text to JSON through semanticparsing as an intermediate state. We also design a novel framework thatincludes more modules to ensure the effectiveness of updating strategies in thetext-to-JSON process. Experimental results demonstrate that our approachoutperforms existing zero-shot DST methods on MultiWOZ, exhibiting significantimprovements in Joint Goal Accuracy (JGA) and slot accuracy compared toexisting ICL methods. Our code has been released.

Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking