DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation

• URL: http://arxiv.org/abs/2204.09149v1
• Date: Tue, 19 Apr 2022 22:26:18 GMT
• Title: DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
• Authors: Md Rashad Al Hasan Rony, Ricardo Usbeck, Jens Lehmann
• Abstract summary: 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 a structure-aware knowledge embedding technique. An empirical evaluation demonstrates the effectiveness of DialoKG over state-of-the-art methods on several standard benchmark datasets.
• Score: 9.186215038100904
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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