Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
IEEE Intelligent Systems
Information state and dialogue management in the TRINDI dialogue move engine toolkit
Natural Language Engineering
Response generation in collaborative negotiation
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Mixed initiative in dialogue: an investigation into discourse segmentation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Mechanisms for mixed-initiative human-computer collaborative discourse
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Learning mixed initiative dialog strategies by using reinforcement learning on both conversants
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning Smooth, Human-Like Turntaking in Realtime Dialogue
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
Incremental dialogue processing in a micro-domain
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
A finite-state turn-taking model for spoken dialog systems
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Initiative conflicts in task-oriented dialogue
Computer Speech and Language
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Turn-yielding cues in task-oriented dialogue
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Uncertainty in Spoken Dialogue Management
Proceedings of the 2010 conference on Human Language Technologies -- The Baltic Perspective: Proceedings of the Fourth International Conference Baltic HLT 2010
Information provision-timing control for informational assistance robot
Proceedings of the 6th international conference on Human-robot interaction
Turn-taking cues in a human tutoring corpus
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Recognizing authority in dialogue with an integer linear programming constrained model
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Decisions about turns in multiparty conversation: from perception to action
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Multiparty turn taking in situated dialog: study, lessons, and directions
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Stability and accuracy in incremental speech recognition
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
After dialog went pervasive: separating dialog behavior modeling and task modeling
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
A temporal simulator for developing turn-taking methods for spoken dialogue systems
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Gaze and turn-taking behavior in casual conversational interactions
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on interaction with smart objects, Special section on eye gaze and conversation
Managing chaos: models of turn-taking in character-multichild interactions
Proceedings of the 15th ACM on International conference on multimodal interaction
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Current turn-taking approaches for spoken dialogue systems rely on the speaker releasing the turn before the other can take it. This reliance results in restricted interactions that can lead to inefficient dialogues. In this paper we present a model we refer to as Importance-Driven Turn-Bidding that treats turn-taking as a negotiative process. Each conversant bids for the turn based on the importance of the intended utterance, and Reinforcement Learning is used to indirectly learn this parameter. We find that Importance-Driven Turn-Bidding performs better than two current turn-taking approaches in an artificial collaborative slot-filling domain. The negotiative nature of this model creates efficient dialogues, and supports the improvement of mixed-initiative interaction.