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Dialogue state tracking (DST) is a key component of task-oriented dialogue systems.
“Scaling up pomdps for dialog management: The“summary pomdp”method,”
Jason D Williams and Steve Young, · 2005
Earlier work this paper cites.
“Agenda-based user simulation for bootstrapping a pomdp dialogue system,”
Jost Schatzmann, Blaise Thomson, Karl Weilhammer, Hui Ye, and Steve Young, · 2007
Earlier work this paper cites.
“Deep neural network approach for the dialog state tracking challenge,”
Matthew Henderson, Blaise Thomson, and Steve Young, · 2013
Earlier work this paper cites.
“The dialog state tracking challenge,”
J. Williams, A. Raux, D. Ramachandran, and A. Black, · 2013
Earlier work this paper cites.
“Multi-domain learning and generalization in dialog state tracking,”
Jason Williams, · 2013
Earlier work this paper cites.
“The second dialog state tracking challenge,”
M. Henderson, B. Thomson, and J. Williams, · 2014
Earlier work this paper cites.
“The third dialog state tracking challenge,”
M. Henderson, B. Thomson, and J. Williams, · 2014
Earlier work this paper cites.
“Word-based dialog state tracking with recurrent neural networks,”
M. Henderson, B. Thomson, and S. Young, · 2014
Earlier work this paper cites.
“Web-style ranking and slu combination for dialog state tracking,”
Jason D Williams, · 2014
Cited alongside, same era.
“Adam: A method for stochastic optimization,”
Diederik P. Kingma and Jimmy Ba, · 2014
Cited alongside, same era.
“Multi-domain dialog state tracking using recurrent neural networks,”
N. Mrkšić, D. Séaghdha, B. Thomson, M. Gašić, P.-H. Su, D. Vandyke, T.H. Wen, and S. Young, · 2015
Cited alongside, same era.
“Interactive reinforcement learning for task-oriented dialogue management,”
Pararth Shah, Dilek Hakkani-Tür, and Larry Heck, · 2016
Cited alongside, same era.
“The Fourth Dialog State Tracking Challenge,”
Seokhwan Kim, Luis Fernando D’Haro, Rafael E. Banchs, Jason Williams, and Matthew Henderson, · 2016
Cited alongside, same era.
“The Fifth Dialog State Tracking Challenge,”
“Dialog state tracking with attention-based sequence-to-sequence learning,”
Takaaki Hori, Hai Wang, Chiori Hori, Shinji Watanabe, Bret Harsham, Jonathan Le Roux, John R Hershey, Yusuke Koji, Yi Jing, Zhaocheng Zhu, et al., · 2016
Later among the works it cites.
“Multi-domain joint semantic frame parsing using bi-directional rnn-lstm,”
D. Hakkani-Tür, G. Tur, A. Celikyilmaz, Y.-N. Chen, J. Gao, L. Deng, and Y.-Y. Wang, · 2016
Later among the works it cites.
“Domain adaptation of recurrent neural networks for natural language understanding,”
A. Jaech, L. Heck, and M.Ostendorf, · 2016
Later among the works it cites.
“A multichannel convolutional neural network for cross-language dialog state tracking,”
Hongjie Shi, Takashi Ushio, Mitsuru Endo, Katsuyoshi Yamagami, and Noriaki Horii, · 2016
Later among the works it cites.
“Robust dialog state tracking for large ontologies,”
Franck Dernoncourt, Ji Young Lee, Trung H Bui, and Hung H Bui, · 2017
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Seokhwan Kim, Luis Fernando D’Haro, Rafael E. Banchs, Jason Williams, Matthew Henderson, and Koichiro Yoshino, · 2016
Cited alongside, same era.
“Neural belief tracker: Data-driven dialogue state tracking,”
N. Mrkšić, D. Séaghdha, T.H. Wen, B. Thomson, and S. Young, · 2016
Cited alongside, same era.
“A network-based end-to-end trainable task-oriented dialogue system,”
Tsung-Hsien Wen, David Vandyke, Nikola Mrksic, Milica Gasic, Lina M Rojas-Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young, · 2016
Cited alongside, same era.
“An end-to-end trainable neural network model with belief tracking for task-oriented dialog,”
Bing Liu and Ian Lane, · 2017
Closest in time.
“Towards zero-shot frame semantic parsing for domain scaling,”
A. Bapna, D. Hakkani-Tür, and L. Heck, · 2017
Closest in time.