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Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation.
Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton. 1993 · 1993
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Using markov decision process for learning dialogue strategies
Esther Levin, Roberto Pieraccini, and Wieland Eckert. 1998 · 1998
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto. 1999 · 1999
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Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
James Henderson, Oliver Lemon, and Kallirroi Georgila. 2008 · 2008
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Evaluation of a hierarchical reinforcement learning spoken dialogue system
Heriberto Cuayáhuitl, Steve Renals, Oliver Lemon, and Hiroshi Shimodaira. 2010 · 2010
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Sample-efficient batch reinforcement learning for dialogue management optimization
Olivier Pietquin, Matthieu Geist, Senthilkumar Chandramohan, and Hervé Frezza-Buet. 2011 · 2011
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Pomdp-based dialogue manager adaptation to extended domains
Milica Gašić, Catherine Breslin, Matthew Henderson, Dongho Kim, Martin Szummer, Blaise Thomson, Pirros Tsiakoulis, and Steve Young. 2013 · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013 · 2013
Cited alongside, same era.
Pomdp-based statistical spoken dialog systems: A review
Steve Young, Milica Gašić, Blaise Thomson, and Jason D Williams. 2013 · 2013
Cited alongside, same era.
Knowledge transfer between speakers for personalised dialogue management
Inigo Casanueva, Thomas Hain, Heidi Christensen, Ricard Marxer, and Phil Green. 2015 · 2015
Cited alongside, same era.
Policy committee for adaptation in multi-domain spoken dialogue systems
Milica Gašić, Nikola Mrkšić, Pei-hao Su, David Vandyke, Tsung-Hsien Wen, and Steve Young. 2015 · 2015
Cited alongside, same era.
Learning domain-independent dialogue policies via ontology parameterisation
Zhuoran Wang, Tsung-Hsien Wen, Pei-Hao Su, and Yannis Stylianou. 2015 · 2015
Cited alongside, same era.
Sub-domain modelling for dialogue management with hierarchical reinforcement learning
Paweł Budzianowski, Stefan Ultes, Pei-Hao Su, Nikola Mrkšić, Tsung-Hsien Wen, Inigo Casanueva, Lina M. Rojas Barahona, and Milica Gašić. 2017 · 2017
Later among the works it cites.
A benchmarking environment for reinforcement learning based task oriented dialogue management
Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su, Nikola Mrkšić, Tsung-Hsien Wen, Stefan Ultes, Lina Rojas-Barahona, Steve Young, and Milica Gašić. 2017 · 2017
Later among the works it cites.
Single-model multi-domain dialogue management with deep learning
Alexandros Papangelis and Yannis Stylianou. 2017 · 2017
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Composite Task-Completion Dialogue System via Hierarchical Deep Reinforcement Learning
B. Peng, X. Li, L. Li, J. Gao, A. Celikyilmaz, S. Lee, and K.-F. Wong. 2017 · 2017
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Sample-efficient actor-critic reinforcement learning with supervised data for dialogue management
Pei-Hao Su, Pawel Budzianowski, Stefan Ultes, Milica Gasic, and Steve Young. 2017 · 2017
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Deep reinforcement learning for multi-domain dialogue systems
Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson, and Jacob Carse. 2016 · 2016
Cited alongside, same era.
Continuously learning neural dialogue management
Pei-Hao Su, Milica Gasic, Nikola Mrksic, Lina Rojas-Barahona, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, and Steve Young. 2016 · 2016
Cited alongside, same era.
The Second Dialog State Tracking Challenge
M. Henderson, B. Thomson, and J. Williams. 2014a
Cited in the paper.
Word-based Dialog State Tracking with Recurrent Neural Networks
M. Henderson, B. Thomson, and S. J. Young. 2014b
Cited in the paper.
Later among the works it cites.
Pydial: A multi-domain statistical dialogue system toolkit
Stefan Ultes, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, Dongho Kim, Iñigo Casanueva, Paweł Budzianowski, Nikola Mrkšić, Tsung-Hsien Wen, Milica Gašić, and Steve J. Young. 2017 · 2017
Later among the works it cites.