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A methodology for turn-taking capabilities enhancement in Spoken Dialogue Systems using Reinforcement Learning.
- Source :
-
Computer Speech & Language . Jan2018, Vol. 47, p93-111. 19p. - Publication Year :
- 2018
-
Abstract
- This article introduces a new methodology to enhance an existing traditional Spoken Dialogue System (SDS) with optimal turn-taking capabilities in order to increase dialogue efficiency. A new approach for transforming the traditional dialogue architecture into an incremental one at a low cost is presented: a new turn-taking decision module called the Scheduler is inserted between the Client and the Service. It is responsible for handling turn-taking decisions. Then, a User Simulator which is able to interact with the system using this new architecture has been implemented and used to train a new Reinforcement Learning turn-taking strategy. Compared to a non-incremental and a handcrafted incremental baselines, it is shown to perform better in simulation and in a real live experiment. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08852308
- Volume :
- 47
- Database :
- Academic Search Index
- Journal :
- Computer Speech & Language
- Publication Type :
- Academic Journal
- Accession number :
- 125417402
- Full Text :
- https://doi.org/10.1016/j.csl.2017.07.006