DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation

Md Rashad Al Hasan Rony, Ricardo Usbeck, Jens Lehmann


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.
Anthology ID:
2022.findings-naacl.195
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venues:
Findings | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2557–2571
Language:
URL:
https://aclanthology.org/2022.findings-naacl.195
DOI:
10.18653/v1/2022.findings-naacl.195
Bibkey:
Cite (ACL):
Md Rashad Al Hasan Rony, Ricardo Usbeck, and Jens Lehmann. 2022. DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2557–2571, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation (Rony et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.195.pdf
Code
 rashad101/dialokg
Data
MultiWOZ