从对话中无监督提取对话策略
Unsupervised Extraction of Dialogue Policies from Conversations
AI总结：
针对现有对话数据缺乏有效对话策略提取方案的问题，提出结合LLMs生成规范形式与基于图的可控可解释方法，从对话中无监督提取更优对话流，为对话策略开发提供高效工具。
摘要：
对话策略在面向任务的对话系统开发中至关重要，但其开发与维护颇具挑战，通常需要对话建模领域专家投入大量精力。尽管许多场景下已有大量针对当前任务的对话数据，但人们缺乏能从这些数据中提取对话策略的有效解决方案。本文针对这一空白，首先阐明大语言模型（Large Language Models, LLMs）如何通过将对话转换为由规范形式构成的统一中间表示，在从数据集提取对话策略的过程中发挥关键作用。随后，我们提出一种生成对话策略的新方法，该方法采用可控且可解释的基于图的方法论。通过将跨对话的规范形式组合成流网络，我们发现运行图遍历算法有助于提取对话流。这些对话流相比通过提示LLMs提取的流，能更好地表示底层交互。我们的技术致力于为对话设计者提供更强的掌控力，提供一款提升对话策略开发流程效率的生产力工具。
英文摘要：
Dialogue policies play a crucial role in developing task-oriented dialogue systems, yet their development and maintenance are challenging and typically require substantial effort from experts in dialogue modeling. While in many situations, large amounts of conversational data are available for the task at hand, people lack an effective solution able to extract dialogue policies from this data. In this paper, we address this gap by first illustrating how Large Language Models (LLMs) can be instrumental in extracting dialogue policies from datasets, through the conversion of conversations into a unified intermediate representation consisting of canonical forms. We then propose a novel method for generating dialogue policies utilizing a controllable and interpretable graph-based methodology. By combining canonical forms across conversations into a flow network, we find that running graph traversal algorithms helps in extracting dialogue flows. These flows are a better representation of the underlying interactions than flows extracted by prompting LLMs. Our technique focuses on giving conversation designers greater control, offering a productivity tool to improve the process of developing dialogue policies.
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。
