Knowledge Diffusion for Neural Dialogue Generation

Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, Dawei Yin


Abstract
End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a neural knowledge diffusion (NKD) model to introduce knowledge into dialogue generation. This method can not only match the relevant facts for the input utterance but diffuse them to similar entities. With the help of facts matching and entity diffusion, the neural dialogue generation is augmented with the ability of convergent and divergent thinking over the knowledge base. Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats. The experiment results also show that our model outperforms competitive baseline models significantly.
Anthology ID:
P18-1138
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1489–1498
Language:
URL:
https://aclanthology.org/P18-1138
DOI:
10.18653/v1/P18-1138
Bibkey:
Cite (ACL):
Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, and Dawei Yin. 2018. Knowledge Diffusion for Neural Dialogue Generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1489–1498, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Knowledge Diffusion for Neural Dialogue Generation (Liu et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1138.pdf
Poster:
 P18-1138.Poster.pdf
Code
 liushuman/neural-knowledge-diffusion