Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Art and Business of Speech Recognition: Creating the Noble Voice
Art and Business of Speech Recognition: Creating the Noble Voice
Toward open-microphone engagement for multiparty interactions
Proceedings of the 8th international conference on Multimodal interfaces
Improving human-robot interaction through adaptation to the auditory scene
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Interruption, Resumption and Domain Switching in In-Vehicle Dialogue
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Comparing user simulation models for dialog strategy learning
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Journal of Artificial Intelligence Research
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Spoken dialogue systems typically do not manage the communication channel, instead using fixed values for such features as the amplitude and speaking rate. Yet, the quality of a dialogue can be compromised if the user has difficulty understanding the system. In this proof-of-concept research, we explore using reinforcement learning (RL) to create policies that manage the communication channel to meet the needs of diverse users. Towards this end, we first formalize a preliminary communication channel model, in which users provide explicit feedback regarding issues with the communication channel, and the system implicitly alters its amplitude to accommodate the user's optimal volume. Second, we explore whether RL is an appropriate tool for creating communication channel management strategies, comparing two different hand-crafted policies to policies trained using both a dialogue-length and a novel annoyance cost. The learned policies performed better than hand-crafted policies, with those trained using the annoyance cost learning an equitable tradeoff between users with differing needs and also learning to balance finding a user's optimal amplitude against dialogue-length. These results suggest that RL can be used to create effective communication channel management policies for diverse users.