  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.
