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Goal-oriented dialogue systems typically rely on components specifically developed for a single task or domain.
The ATIS spoken language systems pilot corpus
Charles T Hemphill, John J Godfrey, and George R Doddington. 1990 · 1990
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A k hypotheses+ other belief updating model
Dan Bohus and Alex Rudnicky. 2006 · 2006
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Partially observable markov decision processes for spoken dialog systems
Jason D Williams and Steve Young. 2007 · 2007
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Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Blaise Thomson and Steve Young. 2010 · 2010
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Deep neural network approach for the dialog state tracking challenge
Matthew Henderson, Blaise Thomson, and Steve Young. 2013 · 2013
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Recipe for building robust spoken dialog state trackers: Dialog state tracking challenge system description
Sungjin Lee and Maxine Eskenazi. 2013 · 2013
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A simple and generic belief tracking mechanism for the dialog state tracking challenge: On the believability of observed information
Zhuoran Wang and Oliver Lemon. 2013 · 2013
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ADAM: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
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Towards end-to-end reinforcement learning of dialogue agents for information access
Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, and Li Deng. 2016 · 2016
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Dialog state tracking, a machine reading approach using memory network
Julien Perez and Fei Liu. 2016 · 2016
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Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2016 · 2016
End-to-end optimization of task-oriented dialogue model with deep reinforcement learning
Bing Liu, Gokhan Tur, Dilek Hakkani-Tur, Pararth Shah, and Larry Heck. 2017 · 2017
Later among the works it cites.
Neural belief tracker: Data-driven dialogue state tracking
Nikola Mrkšić, Diarmuid O Séaghdha, Tsung-Hsien Wen, Blaise Thomson, and Steve Young. 2017 · 2017
Later among the works it cites.
Generating high-quality and informative conversation responses with sequence-to-sequence models
Louis Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, and Ray Kurzweil. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Overview of the sixth dialog system technology challenge: Dstc6
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Cited alongside, same era.
The dialog state tracking challenge series: A review
Jason Williams, Antoine Raux, and Matthew Henderson. 2016 · 2016
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An attentional neural conversation model with improved specificity
Kaisheng Yao, Baolin Peng, Geoffrey Zweig, and Kam-Fai Wong. 2016 · 2016
Cited alongside, same era.
Tiancheng Zhao and Maxine Eskenazi. 2016 · 2016
Cited alongside, same era.
The second dialog state tracking challenge
Matthew Henderson, Blaise Thomson, and Jason D Williams. 2014a
Cited in the paper.
Word-based dialog state tracking with recurrent neural networks
Matthew Henderson, Blaise Thomson, and Steve Young. 2014b
Cited in the paper.
Chiori Hori, Julien Perez, Ryuichi Higasinaka, Takaaki Hori, Y-Lan Boureau, Michimasa Inaba, Yuiko Tsunomori, Tetsuro Takahashi, Koichiro Yoshino, and Seokhwan Kim. 2018 · 2018
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Bing Liu, Gokhan Tur, Dilek Hakkani-Tur, Pararth Shah, and Larry Heck. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Multi-task learning for joint language understanding and dialogue state tracking
Abhinav Rastogi, Raghav Gupta, and Dilek Hakkani-Tur. 2018 · 2018
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