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Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System
2014
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multidomain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection problem is treated as sequential planning instead of classification, such that confirmation and clarification interaction mechanisms are supported. In addition, it is shown that by using a model parameter tying trick, the extensibility of the system can be
doi:10.3115/v1/d14-1007
dblp:conf/emnlp/WangCWTWW14
fatcat:4s2ysnzx4rei5ioftyfgeb2gyu