DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation

• DOI: 10.18653/v1/2022.findings-naacl.195
• Publication Date: 2022-07-26T02:59:46Z
• Authors: Md Rashad Al Hasa...; Ricardo Usbeck; Jens Lehmann
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 01 natural sciences; Computation and Language (cs.CL); 0105 earth and related environmental sciences

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.<br/>Accepted by the North American Chapter of the Association for Computational Linguistics (NAACL) 2022<br/>