DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
This work addresses the problem of dynamic knowledge integration in dialogue systems for applications like customer service, though it appears incremental by building on existing knowledge distillation methods.
The paper tackled the challenge of generating human-like and informative responses in task-oriented dialogue systems by incorporating structural information from knowledge graphs, resulting in improved performance over state-of-the-art methods on standard benchmarks.
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.