More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge

Sixing Wu, Ying Li, Minghui Wang, Dawei Zhang, Yang Zhou, Zhonghai Wu


Abstract
Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage. Thus, they often degenerate into traditional methods because not all dialogues can be linked with knowledge entries. This paper proposes a novel dialogue generation model, MSKE-Dialog, to solve this issue with three unique advantages: (1) Rather than only one, MSKE-Dialog can simultaneously leverage multiple heterogeneous knowledge sources (it includes but is not limited to commonsense knowledge facts, text knowledge, infobox knowledge) to improve the knowledge coverage; (2) To avoid the topic conflict among the context and different knowledge sources, we propose a Multi-Reference Selection to better select context/knowledge; (3) We propose a Multi-Reference Generation to generate informative responses by referring to multiple generation references at the same time. Extensive evaluations on a Chinese dataset show the superior performance of this work against various state-of-the-art approaches. To our best knowledge, this work is the first to use the multi-source heterogeneous knowledge in the open-domain knowledge-enhanced dialogue generation.
Anthology ID:
2021.emnlp-main.175
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2286–2300
Language:
URL:
https://aclanthology.org/2021.emnlp-main.175
DOI:
10.18653/v1/2021.emnlp-main.175
Bibkey:
Cite (ACL):
Sixing Wu, Ying Li, Minghui Wang, Dawei Zhang, Yang Zhou, and Zhonghai Wu. 2021. More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2286–2300, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge (Wu et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.175.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.175.mp4
Code
 pku-sixing/emnlp2021-mske_dialog
Data
ConceptNet