We propose a set of generic conversational strategies to handle possible sys-tem breakdowns in non-task-oriented dialog systems. We also design dialog policies to select among these strategies with respect to different dialog contexts. We combine expert knowledge and the statistical findings that derived from previous collected data in designing these policies. The dialog policy learned via reinforcement learning outperforms the random selection policy and the locally greedy policy in both the simulated and the real-world settings. In addition, we propose three metrics, which consider both the lo-cal and global quality of the conversation, to evaluate conversation quality.
Strategy and Policy Learning for Non-Task-Oriented Conversational Systems
Zhou Yu,Ziyu Xu,A. Black,Alexander I. Rudnicky
Published 2016 in SIGDIAL Conference
ABSTRACT
PUBLICATION RECORD
- Publication year
2016
- Venue
SIGDIAL Conference
- Publication date
2016-09-01
- 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 1-21 of 21 references · Page 1 of 1