Task-oriented dialogue generation is challenging since the underlying knowledge is often dynamic and effectively incorporating knowledge into the learning process is hard. It is particularly challenging to generate both human-like and informative responses in this setting. Recent research primarily focused on various knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured. In this paper, we go one step further and demonstrate how the structural information of a knowledge graph can improve the system's inference capabilities. Specifically, we propose DialoKG, a novel task-oriented dialogue system that effectively incorporates knowledge into a language model. Our proposed system views relational knowledge as a knowledge graph and introduces (1) a structure-aware knowledge embedding technique, and (2) a knowledge graph-weighted attention masking strategy to facilitate the system selecting relevant information during the dialogue generation. An empirical evaluation demonstrates the effectiveness of DialoKG over state-of-the-art methods on several standard benchmark datasets.

本论文提出一种新的面向任务的对话系统 DialoKG, 通过将关系型知识看作知识图，并引入结构感知知识嵌入技术和知识图加权注意力掩膜策略来有效地将知识嵌入语言模型，实现人类化和信息化的对话。通过在多个标准基准数据集上的实证评估，展示了 DialoKG 对最先进方法的有效性。

DialoKG: 以知识结构为导向的任务型对话生成