Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog (2023)

Abstract: 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 at https://github.com/18907305772/MAKER .

## introduction when task-oriented dialog (tod) systems try to accomplish a task such as restaurant reservations and weather reporting for human users, they generally resort to an external knowledge base (kb) to retrieve relevant entity information for generating an informative system response. conventional pipeline systems comprise several modules such as dialogue state tracking and dialogue policy learning that require annotations for training, where intermediate predictions such as belief state can be used for the retrieval. by contrast, end-to-end task-oriented dialog (e2e-tod) systems aim to eliminate the dependence on intermediate annotations and generate the response end-to-end @xcite . apparently, knowledge retrieval is at the core of this task, ## methods in this section, we first describe the notations and outline our method, and then introduce the knowledge retriever and response generator in detail. ## results and analysis in this section, we first show the overall performance of the evaluated systems given a condensed knowledge base for each dialog. then, we compare them with a more practical setting in which a large-scale knowledge base is provided. we also conduct an in-depth analysis of the proposed retriever. more experiments are presented in the appendix. ## conclusion we propose a novel multi-grained knowledge retriever (maker) for end-to-end task-oriented dialog systems. it decouples knowledge retrieval from response generation and introduces an entity selector and an attribute selector to acquire multigrained knowledge from the knowledge base. the retriever is trained by distilling knowledge from the response generator. empirical results show that our system achieves state-of-the-art performance when either a small or a large-scale knowledge base is provided for each dialog. through in-depth analysis, our retriever shows great advantages over baselines when the size of knowledge bases grows large. of the two selectors, the entity selector is shown to be more prominent in the retriever.