Task-oriented dialogue systems continue to face significant challenges as they require not only an understanding of dialogue history but also domain-specific knowledge. However, knowledge is often dynamic, making it difficult to effectively integrate into the learning process. Existing large language model approaches primarily treat knowledge bases as textual resources, neglecting to capture the underlying relationships between facts within the knowledge base. To address this limitation, we propose a novel dialogue system called PluDG. We regard the knowledge as a knowledge graph and propose a knowledge extraction plug-in, Kg-Plug, to capture the features of the graph and generate prompt entities to assist the system's dialogue generation. Besides, we propose Unified Memory Integration, a module that enhances the comprehension of the sentence's internal structure and optimizes the knowledge base's encoding location. We conduct experiments on three public datasets and compare PluDG with several state-of-the-art dialogue models. The experimental results indicate that PluDG achieves significant improvements in both accuracy and diversity, outperforming the current state-of-the-art dialogue system models and achieving state-of-the-art performance.
Keywords: Artificial intelligence; Data science; Dialogue systems; Graph neural networks; Natural language processing.
©2023 Dong and Chen.