Responses generated by neural conversational models tend to lack informativeness and diversity. We present Adversarial Information Maximization (AIM), an adversarial learning framework that addresses these two related but distinct problems. To foster response diversity, we leverage adversarial training that allows distributional matching of synthetic and real responses. To improve informativeness, our framework explicitly optimizes a variational lower bound on pairwise mutual information between query and response. Empirical results from automatic and human evaluations demonstrate that our methods significantly boost informativeness and diversity.
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Yizhe Zhang,Michel Galley,Jianfeng Gao,Zhe Gan,Xiujun Li,Chris Brockett,W. Dolan
Published 2018 in Neural Information Processing Systems
ABSTRACT
PUBLICATION RECORD
- Publication year
2018
- Venue
Neural Information Processing Systems
- Publication date
2018-09-16
- Fields of study
Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.
REFERENCES
Showing 1-46 of 46 references · Page 1 of 1