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This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST).
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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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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Steve Young. 2010 · 2010
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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
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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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GloVe: Global vectors for word representation
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SimLex-999: Evaluating semantic models with (genuine) similarity estimation
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Adam: A method for stochastic optimization
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PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification
Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch. 2015 · 2015
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From paraphrase database to compositional paraphrase model and back
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2015 · 2015
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Counter-fitting word vectors to linguistic constraints
Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Lina Rojas-Barahona, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, and Steve Young. 2016 · 2016
Cited alongside, same era.
On-line active reward learning for policy optimisation in spoken dialogue systems
Pei-Hao Su, Milica Gašić, Nikola Mrkšić, Lina Rojas-Barahona, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, and Steve Young. 2016 · 2016
The Dialog State Tracking Challenge series: A review
Jason D. Williams, Antoine Raux, and Matthew Henderson. 2016 · 2016
Later among the works it cites.
Gated end-to-end memory networks
Fei Liu and Julien Perez. 2017 · 2017
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Sample-efficient actor-critic reinforcement learning with supervised data for dialogue management
Pei-Hao Su, Paweł Budzianowski, Stefan Ultes, Milica Gašić, and Steve Young. 2017 · 2017
Later among the works it cites.
Hybrid dialog state tracker with ASR features
Miroslav Vodolán, Rudolf Kadlec, and Jan Kleindienst. 2017 · 2017
Later among the works it cites.
Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules
Ivan Vulić, Nikola Mrkšić, Roi Reichart, Diarmuid Ó Séaghdha, Steve Young, and Anna Korhonen. 2017 · 2017
Later among the works it cites.
A network-based end-to-end trainable task-oriented dialogue system
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Cited alongside, same era.
Recurrent polynomial network for dialogue state tracking
Kai Sun, Qizhe Xie, and Kai Yu. 2016 · 2016
Cited alongside, same era.
Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised adaptation
Matthew Henderson, Blaise Thomson, and Steve Young. 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.
Neural Belief Tracker: Data-driven dialogue state tracking
Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Tsung-Hsien Wen, and Steve Young. 2017a
Cited in the paper.
Semantic specialisation of distributional word vector spaces using monolingual and cross-lingual constraints
Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, Ira Leviant, Roi Reichart, Milica Gašić, Anna Korhonen, and Steve Young. 2017b
Cited in the paper.
Tsung-Hsien Wen, David Vandyke, Nikola Mrkšić, Milica Gašić, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young. 2017 · 2017
Later among the works it cites.