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In this work, we present a hybrid learning method for training task-oriented dialogue systems through online user interactions.
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A copy-augmented sequence-to-sequence architecture gives good performance on task-oriented dialogue
Mihail Eric and Christopher D Manning. 2017 · 2017
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End-to-end task-completion neural dialogue systems
Xuijun Li, Yun-Nung Chen, Lihong Li, and Jianfeng Gao. 2017 · 2017
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Bing Liu, Gokhan Tur, Dilek Hakkani-Tur, Pararth Shah, and Larry Heck. 2017 · 2017
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Interactive reinforcement learning for task-oriented dialogue management
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On-line active reward learning for policy optimisation in spoken dialogue systems
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Jason D Williams and Geoffrey Zweig. 2016 · 2016
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Composite task-completion dialogue policy learning via hierarchical deep reinforcement learning
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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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A network-based end-to-end trainable task-oriented dialogue system
Tsung-Hsien Wen, David Vandyke, Nikola Mrkšić, Milica Gašić, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young. 2017 · 2017
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Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
Jason D Williams, Kavosh Asadi, and Geoffrey Zweig. 2017 · 2017
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Bootstrapping a neural conversational agent with dialogue self-play, crowdsourcing and on-line reinforcement learning
Pararth Shah, Dilek Hakkani-Tür, Liu Bing, and Gokhan Tür. 2018 · 2018
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