Retrieving proper domain knowledge from an external database lies at the heart of end-to-end task-oriented dialog systems to generate informative responses. Most existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses, leading to suboptimal retrieval performance when the knowledge base becomes large-scale. To address this, we propose to decouple knowledge retrieval from response generation and introduce a multi-grained knowledge retriever (MAKER) that includes an entity selector to search for relevant entities and an attribute selector to filter out irrelevant attributes. To train the retriever, we propose a novel distillation objective that derives supervision signals from the response generator. Experiments conducted on three standard benchmarks with both small and large-scale knowledge bases demonstrate that our retriever performs knowledge retrieval more effectively than existing methods. Our code has been made publicly available.\footnote{https://github.com/18907305772/MAKER}

翻译：从外部数据库中检索适当的领域知识是端到端任务导向对话系统生成信息性响应的核心。现有大多数系统将知识检索与响应生成相结合，并通过参考响应的直接监督进行优化，导致知识库规模扩大时检索性能不佳。为解决此问题，我们提出将知识检索与响应生成解耦，并引入一个多粒度知识检索器（MAKER），该检索器包含一个实体选择器用于搜索相关实体，以及一个属性选择器用于过滤不相关属性。为训练该检索器，我们提出一种新颖的蒸馏目标，从响应生成器中获取监督信号。在三个标准基准数据集（包含小规模和大规模知识库）上进行的实验表明，我们的检索器比现有方法更有效地执行知识检索。我们的代码已开源。\footnote{https://github.com/18907305772/MAKER}