用于零样本对话状态跟踪复杂更新策略的大语言模型语义解析
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
AI总结：
提出ParsingDST方法，利用大语言模型语义解析将对话转为JSON，引入复杂更新策略，在MultiWOZ上显著提升零样本DST的JGA和槽位准确率。
摘要：
零样本对话状态跟踪（DST）应对获取和标注任务导向对话的挑战，这既耗时又昂贵。然而，DST超越了简单的槽位填充，需要有效的更新策略来随着对话推进跟踪对话状态。本文提出ParsingDST，一种新的上下文学习（ICL）方法，在零样本DST中引入额外的复杂更新策略。该方法通过利用强大的大语言模型（LLMs）重构DST任务，通过语义解析将原始对话文本转换为JSON作为中间状态。我们还设计了一个包含更多模块的新框架，以确保文本到JSON过程中更新策略的有效性。实验结果表明，该方法在MultiWOZ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）和槽位准确率上表现出显著提升。代码已发布。
英文摘要：
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. Our code has been released.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
