Paper Info
Title
POMDP-based Let's Go system for spoken dialog challenge
Abstract
This paper describes a POMDP-based Let's Go system which incorporates belief tracking and dialog policy optimization into the dialog manager of the reference system for the Spoken Dialog Challenge (SDC). Since all components except for the dialog manager were kept the same, component-wise comparison can be performed to investigate the effect of belief tracking and dialog policy optimization on the overall system performance. In addition, since unsupervised methods have been adopted to learn all required models to reduce human labor and development time, the effectiveness of the unsupervised approaches compared to conventional supervised approaches can be investigated. The result system participated in the 2011 SDC and showed comparable performance with the base system which has been enhanced from the reference system for the 2010 SDC. This shows the capability of the proposed method to rapidly produce an effective system with minimal human labor and experts' knowledge.
Year
DOI
Venue
2012
10.1109/SLT.2012.6424198
SLT
Keywords
Field
DocType
base system,optimisation,partially observable markov decision process,pomdp-based spoken dialog system,belief tracking,let's go,2010 sdc,dialog policy optimization,spoken dialog challenge,unsupervised method,speaker recognition,2011 sdc,dialog manager,interactive systems,markov processes,unsupervised learning,pomdp-based let's go system
Dialog box,Markov process,Spoken dialog,Computer science,Partially observable Markov decision process,Speech recognition,Unsupervised learning,Speaker recognition,Dialog system,Natural language processing,Artificial intelligence
Conference
ISSN
ISBN
Citations 
2639-5479
978-1-4673-5124-9
7
PageRank 
References 
Authors
0.56
13
2
Name
Order
Citations
PageRank
Sungjin Lee122127.44
Maxine Eskenazi2979127.53