Fetching the paper…
Reading the bibliography…
Dialogue state tracking (DST) is a crucial module in dialogue management.
Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra. 1998 · 1998
Earlier work this paper cites.
A tractable hybrid ddn–pomdp approach to affective dialogue modeling for probabilistic frame-based dialogue systems
Trung H Bui, Mannes Poel, Anton Nijholt, and Job Zwiers. 2009 · 2009
Earlier work this paper cites.
The hidden information state model: A practical framework for pomdp-based spoken dialogue management
Steve Young, Milica Gašić, Simon Keizer, François Mairesse, Jost Schatzmann, Blaise Thomson, and Kai Yu. 2010 · 2010
Earlier work this paper cites.
Reinforcement learning for parameter estimation in statistical spoken dialogue systems
Filip Jurčíček, Blaise Thomson, and Steve Young. 2012 · 2012
Earlier work this paper cites.
Deep neural network approach for the dialog state tracking challenge
Matthew Henderson, Blaise Thomson, and Steve Young. 2013 · 2013
Earlier work this paper cites.
Structured discriminative model for dialog state tracking
Sungjin Lee. 2013 · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller. 2014 · 2014
Earlier work this paper cites.
Web-style ranking and slu combination for dialog state tracking
Jason D Williams. 2014 · 2014
Cited alongside, same era.
Strategic dialogue management via deep reinforcement learning
Heriberto Cuayáhuitl, Simon Keizer, and Oliver Lemon. 2015 · 2015
Cited alongside, same era.
Deep reinforcement learning in parameterized action space
Matthew Hausknecht and Peter Stone. 2015 · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015 · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. 2015 · 2015
Cited alongside, same era.
The dialog state tracking challenge series: A review
Jason Williams, Antoine Raux, and Matthew Henderson. 2016 · 2016
Later among the works it cites.
Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning
Tiancheng Zhao and Maxine Eskenazi. 2016 · 2016
Later among the works it cites.
Affordable on-line dialogue policy learning
Cheng Chang, Runzhe Yang, Lu Chen, Xiang Zhou, and Kai Yu. 2017 · 2017
Later among the works it cites.
On-line dialogue policy learning with companion teaching
Lu Chen, Runzhe Yang, Cheng Chang, Zihao Ye, Xiang Zhou, and Kai Yu. 2017 · 2017
Later among the works it cites.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine. 2017 · 2017
Later among the works it cites.
An end-to-end trainable neural network model with belief tracking for task-oriented dialog
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Constrained markov bayesian polynomial for efficient dialogue state tracking
Kai Yu, Kai Sun, Lu Chen, and Su Zhu. 2015 · 2015
Cited alongside, same era.
Policy networks with two-stage training for dialogue systems
Mehdi Fatemi, Layla El Asri, Hannes Schulz, Jing He, and Kaheer Suleman. 2016 · 2016
Cited alongside, same era.
Efficient exploration for dialogue policy learning with bbq networks & replay buffer spiking
Zachary C Lipton, Jianfeng Gao, Lihong Li, Xiujun Li, Faisal Ahmed, and Li Deng. 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.
The third dialog state tracking challenge
Matthew Henderson, Blaise Thomson, and Jason D Williams. 2014b
Cited in the paper.
Word-based dialog state tracking with recurrent neural networks
Matthew Henderson, Blaise Thomson, and Steve Young. 2014c
Cited in the paper.
Agenda-based user simulation for bootstrapping a pomdp dialogue system
Jost Schatzmann, Blaise Thomson, Karl Weilhammer, Hui Ye, and Steve Young. 2007a
Cited in the paper.
Bing Liu and Ian Lane. 2017 · 2017
Later among the works it cites.
Sample-efficient actor-critic reinforcement learning with supervised data for dialogue management
Pei-Hao Su, Paweł Budzianowski, Stefan Ultes, Milica Gasic, and Steve Young. 2017 · 2017
Later among the works it cites.
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
Later among the works it cites.