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Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
Is learning the n-th thing any easier than learning the first?
Sebastian Thrun. 1996 · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Multitask learning
Rich Caruana. 1998 · 1998
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Learning to learn with the informative vector machine
Neil D Lawrence and John C Platt. 2004 · 2004
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Semiparametric latent factor models
Matthias Seeger, Yee-Whye Teh, and Michael Jordan. 2005 · 2005
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Learning gaussian processes from multiple tasks
Kai Yu, Volker Tresp, and Anton Schwaighofer. 2005 · 2005
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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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Multi-task gaussian process prediction
Edwin V Bonilla, Kian M Chai, and Christopher Williams. 2008 · 2008
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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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Discriminative spoken language understanding using word confusion networks
Matthew Henderson, Milica Gašić, Blaise Thomson, Pirros Tsiakoulis, Kai Yu, and Steve Young. 2012 · 2012
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PPDB: The paraphrase database
Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch. 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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The dialog state tracking challenge
Jason D Williams, Antoine Raux, Deepak Ramachandran, and Alan Black. 2013 · 2013
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POMDP-based statistical spoken dialog systems: A review
Steve Young, Milica Gašić, Blaise Thomson, and Jason D Williams. 2013 · 2013
Cited alongside, same era.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 2014
Cited alongside, same era.
Web-style ranking and slu combination for dialog state tracking
Jason D Williams. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
A joint many-task model: Growing a neural network for multiple NLP tasks
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017 · 2017
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Deep semantic role labeling: What works and what’s next
Luheng He, Kenton Lee, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
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Lukasz Kaiser, Aidan N Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, and Jakob Uszkoreit. 2017 · 2017
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End-to-end neural coreference resolution
Kenton Lee, Luheng He, Mike Lewis, and Luke S. Zettlemoyer. 2017 · 2017
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Neural belief tracker: Data-driven dialogue state tracking
Nikola Mrkšić, Diarmuid O Séaghdha, Tsung-Hsien Wen, Blaise Thomson, and Steve Young. 2017 · 2017
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
Cited alongside, same era.
Multi-domain dialog state tracking using recurrent neural networks
Nikola Mrkšić, Diarmuid O Séaghdha, Blaise Thomson, Milica Gašić, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, and Steve Young. 2015 · 2015
Cited alongside, same era.
From paraphrase database to compositional paraphrase model and back
John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu, and Dan Roth. 2015 · 2015
Cited alongside, same era.
Incremental LSTM-based dialog state tracker
Lukas Zilka and Filip Jurcicek. 2015 · 2015
Cited alongside, same era.
Long short-term memory-networks for machine reading
Jianpeng Cheng, Li Dong, and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Cited alongside, same era.
Julien Perez and Fei Liu. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter Liu, and Christopher Manning. 2017 · 2017
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Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 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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Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2017 · 2017
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Closest in time.
DCN+: Mixed objective and deep residual coattention for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2018 · 2018
Closest in time.