DAL: Dual Adversarial Learning for Dialogue Generation

Shaobo Cui, Rongzhong Lian, Di Jiang, Yuanfeng Song, Siqi Bao, Yong Jiang


Abstract
In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning(DAL) for high-quality response generation. DAL innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms state-of-the-art methods regarding automatic metrics and human evaluations.
Anthology ID:
W19-2302
Volume:
Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Antoine Bosselut, Asli Celikyilmaz, Marjan Ghazvininejad, Srinivasan Iyer, Urvashi Khandelwal, Hannah Rashkin, Thomas Wolf
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–20
Language:
URL:
https://aclanthology.org/W19-2302
DOI:
10.18653/v1/W19-2302
Bibkey:
Cite (ACL):
Shaobo Cui, Rongzhong Lian, Di Jiang, Yuanfeng Song, Siqi Bao, and Yong Jiang. 2019. DAL: Dual Adversarial Learning for Dialogue Generation. In Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation, pages 11–20, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
DAL: Dual Adversarial Learning for Dialogue Generation (Cui et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2302.pdf