Task-oriented dialogues often require agents to enact complex, multi-step procedures in order to meet user requests. While large language models have found success automating these dialogues in constrained environments, their widespread deployment is limited by the substantial quantities of task-specific data required for training. The following paper presents a data-efficient solution to constructing dialogue systems, leveraging explicit instructions derived from agent guidelines, such as company policies or customer service manuals. Our proposed Knowledge-Augmented Dialogue System (KADS) combines a large language model with a knowledge retrieval module that pulls documents outlining relevant procedures from a predefined set of policies, given a user-agent interaction. To train this system, we introduce a semi-supervised pre-training scheme that employs dialogue-document matching and action-oriented masked language modeling with partial parameter freezing. We evaluate the effectiveness of our approach on prominent task-oriented dialogue datasets, Action-Based Conversations Dataset and Schema-Guided Dialogue, for two dialogue tasks: action state tracking and workflow discovery. Our results demonstrate that procedural knowledge augmentation improves accuracy predicting in- and out-of-distribution actions while preserving high performance in settings with low or sparse data.

翻译：任务导向型对话通常要求智能体执行复杂的多步骤程序以满足用户请求。虽然大型语言模型在受限环境中已成功实现这类对话的自动化，但其广泛部署受限于训练所需的大量任务特定数据。本文提出了一种数据高效的对话系统构建方案，通过利用来自智能体指南（如公司政策或客服手册）的显式指令来实现。我们提出的知识增强对话系统（KADS）将大型语言模型与知识检索模块相结合，该模块可根据用户-智能体交互从预定义策略集合中提取相关程序文档。为训练该系统，我们引入了一种半监督预训练方案，该方案采用对话-文档匹配和面向动作的掩码语言建模，并辅以部分参数冻结。我们在两个主流任务导向型对话数据集——基于动作的对话数据集和模式引导对话——上评估了该方法在动作状态追踪和工作流发现两项对话任务中的有效性。结果表明，程序性知识增强能够提升分布内和分布外动作的预测准确率，同时在低数据或稀疏数据场景下保持高性能。