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In dialogue systems, a dialogue state tracker aims to accurately find a compact representation of the current dialogue status, based on the entire dialogue history.
Multiwoz 2.1: Multi-domain dialogue state corrections and state tracking baselines
Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyag Gao, and Dilek Hakkani-Tur. 2019 · 1907
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Find or classify? dual strategy for slot-value predictions on multi-domain dialog state tracking
Jian-Guo Zhang, Kazuma Hashimoto, Chien-Sheng Wu, Yao Wan, Philip S Yu, Richard Socher, and Caiming Xiong. 2019 · 1910
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Efficient dialogue state tracking by selectively overwriting memory
Sungdong Kim, Sohee Yang, Gyuwan Kim, and Sang-Woo Lee. 2020 · 1911
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A joint many-task model: Growing a neural network for multiple nlp tasks
Kazuma Hashimoto, Yoshimasa Tsuruoka, Richard Socher, et al. 2017 · 1933
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Automatic dialogue summary generation for customer service
Chunyi Liu, Peng Wang, Jiang Xu, Zang Li, and Jieping Ye. 2019 · 1965
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A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser. 1989 · 1989
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour. 2000 · 2000
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Automated construction of database interfaces: Integrating statistical and relational learning for semantic parsing
Lappoon R Tang and Raymond J Mooney. 2000 · 2000
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Natural actor and belief critic: Reinforcement algorithm for learning parameters of dialogue systems modelled as pomdps
Filip Jurčíček, Blaise Thomson, and Steve Young. 2011 · 2011
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Semantic parsing as machine translation
Jacob Andreas, Andreas Vlachos, and Stephen Clark. 2013 · 2013
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Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing
Yun-Nung Chen, William Yang Wang, and Alexander I Rudnicky. 2013 · 2013
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The second dialog state tracking challenge
Matthew Henderson, Blaise Thomson, and Jason D Williams. 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
The sjtu system for dialog state tracking challenge 2
Kai Sun, Lu Chen, Su Zhu, and Kai Yu. 2014 · 2014
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Semantic parser enhancement for dialogue domain extension with little data
Su Zhu, Lu Chen, Kai Sun, Da Zheng, and Kai Yu. 2014 · 2014
Cited alongside, same era.
Effective approaches to attention-based neural machine translation
Dialog state tracking, a machine reading approach using memory network
Julien Perez and Fei Liu. 2017 · 2017
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Multiwoz-a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gasic. 2018 · 2018
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Coarse-to-fine decoding for neural semantic parsing
Li Dong and Mirella Lapata. 2018 · 2018
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Towards universal dialogue state tracking
Liliang Ren, Kaige Xie, Lu Chen, and Kai Yu. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Dialog state tracking: A neural reading comprehension approach
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Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015 · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
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Type-driven incremental semantic parsing with polymorphism
Kai Zhao and Liang Huang. 2015 · 2015
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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. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Cited alongside, same era.
Shuyang Gao, Abhishek Sethi, Sanchit Agarwal, Tagyoung Chung, Dilek Hakkani-Tur, and Amazon Alexa AI. 2019 · 2019
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Hyst: A hybrid approach for flexible and accurate dialogue state tracking
Rahul Goel, Shachi Paul, and Dilek Hakkani-Tür. 2019 · 2019
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Sumbt: Slot-utterance matching for universal and scalable belief tracking
Hwaran Lee, Jinsik Lee, and Tae-Yoon Kim. 2019 · 2019
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Scalable and accurate dialogue state tracking via hierarchical sequence generation
Liliang Ren, Jianmo Ni, and Julian McAuley. 2019 · 2019
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Transferable multi-domain state generator for task-oriented dialogue systems
Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl, Caiming Xiong, Richard Socher, and Pascale Fung. 2019 · 2019
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