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The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains.
P. Price, “Evaluation of spoken language systems: The ATIS domain,” in
1990
Earlier work this paper cites.
L. A. Ramshaw and M. P. Marcus, “Text chunking using transformation-based learning,” in
1995
Earlier work this paper cites.
H. Meng, S. Busayapongchai, J. Giass, D. Goddeau, L. Hethetingron, E. Hurley, C. Pao, J. Polifroni, S. Seneff, and V. Zue, “Wheels: A conversational system in the automobile classifieds domain,” in
1996
Earlier work this paper cites.
R. Caruana, “Multitask learning,”
1997
Earlier work this paper cites.
J. R. Glass and T. J. Hazen, “Telephone-based conversational speech recognition in the JUPITER domain.” in
1998
Earlier work this paper cites.
E. F. Tjong Kim Sang and S. Buchholz, “Introduction to the conll-2000 shared task: Chunking,” in
2000
Earlier work this paper cites.
R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in
2008
Earlier work this paper cites.
G. Tur, D. Hakkani-Tur, and L. Heck, “What is left to be understood in ATIS?” in
2010
Earlier work this paper cites.
X. Li, Y.-Y. Wang, and G. Tür, “Multi-task learning for spoken language understanding with shared slots.” in
2011
Earlier work this paper cites.
M. Sundermeyer, R. Schlüter, and H. Ney, “Lstm neural networks for language modeling.” in
2012
Earlier work this paper cites.
A. Deoras and R. Sarikaya, “Deep belief network based semantic taggers for spoken language understanding.” in
2013
Cited alongside, same era.
G. Mesnil, X. He, L. Deng, and Y. Bengio, “Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding.” in
2013
Cited alongside, same era.
K. Yao, G. Zweig, M.-Y. Hwang, Y. Shi, and D. Yu, “Recurrent neural networks for language understanding.” in
2013
Cited alongside, same era.
P. Xu and R. Sarikaya, “Convolutional neural network based triangular crf for joint intent detection and slot filling,” in
2013
Cited alongside, same era.
M. Gašic, C. Breslin, M. Henderson, D. Kim, M. Szummer, B. Thomson, P. Tsiakoulis, and S. Young, “POMDP-based dialogue manager adaptation to extended domains,” in
2013
Cited alongside, same era.
Y. Shi, K. Yao, H. Chen, Y.-C. Pan, M.-Y. Hwang, and B. Peng, “Contextual spoken language understanding using recurrent neural networks,” in
2015
Later among the works it cites.
G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L. Deng, D. Hakkani-Tur, X. He, L. Heck, G. Tur, D. Yu
2015
Later among the works it cites.
B. Peng, K. Yao, L. Jing, and K.-F. Wong, “Recurrent neural networks with external memory for spoken language understanding,” in
2015
Later among the works it cites.
2015
Later among the works it cites.
R. K. Srivastava, K. Greff, and J. Schmidhuber, “Highway networks,”
2015
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K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, and Y. Shi, “Spoken language understanding using long short-term memory neural networks,” in
2014
Cited alongside, same era.
M. Henderson, B. Thomson, and S. Young, “Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised adaptation,” in
2014
Cited alongside, same era.
A. El-Kahky, X. Liu, R. Sarikaya, G. Tür, D. Hakkani-Tür, and L. Heck, “Extending domain coverage of language understanding systems via intent transfer between domains using knowledge graphs and search query click logs,” in
2014
Cited alongside, same era.
H. Sak, A. W. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling.” in
2014
Cited alongside, same era.
W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent neural network regularization,”
2014
Cited alongside, same era.
Later among the works it cites.
2016
Closest in time.
L. Dong and M. Lapata, “Language to logical form with neural attention,”
2016
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T. N. Vu, P. Gupta, H. Adel, and H. Schütze, “Bi-directional recurrent neural network with ranking loss for spoken language understanding,” in
2016
Closest in time.