DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
Md Rashad Al Hasan Rony, Ricardo Usbeck, Jens Lehmann

TL;DR
DialoKG is a novel dialogue system that leverages knowledge graph structures through specialized embedding and attention strategies to generate more informative and human-like responses in task-oriented dialogues.
Contribution
It introduces a structure-aware knowledge embedding and a graph-weighted attention mechanism to better incorporate knowledge graphs into dialogue generation.
Findings
Outperforms state-of-the-art methods on benchmark datasets.
Effectively captures relational knowledge for improved responses.
Enhances inference capabilities in knowledge-based dialogue systems.
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
MethodsBalanced Selection · Knowledge Distillation
