Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness

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


Abstract
Generative dialogue systems tend to produce generic responses, which often leads to boring conversations. For alleviating this issue, Recent studies proposed to retrieve and introduce knowledge facts from knowledge graphs. While this paradigm works to a certain extent, it usually retrieves knowledge facts only based on the entity word itself, without considering the specific dialogue context. Thus, the introduction of the context-irrelevant knowledge facts can impact the quality of generations. To this end, this paper proposes a novel commonsense knowledge-aware dialogue generation model, ConKADI. We design a Felicitous Fact mechanism to help the model focus on the knowledge facts that are highly relevant to the context; furthermore, two techniques, Context-Knowledge Fusion and Flexible Mode Fusion are proposed to facilitate the integration of the knowledge in the ConKADI. We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation. Extensive evaluations over both an open-released English dataset and our Chinese dataset demonstrate that our approach ConKADI outperforms the state-of-the-art approach CCM, in most experiments.
Anthology ID:
2020.acl-main.515
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5811–5820
Language:
URL:
https://aclanthology.org/2020.acl-main.515
DOI:
10.18653/v1/2020.acl-main.515
Bibkey:
Cite (ACL):
Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, and Zhonghai Wu. 2020. Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5811–5820, Online. Association for Computational Linguistics.
Cite (Informal):
Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness (Wu et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.515.pdf
Video:
 http://slideslive.com/38928749