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An essential component of spoken language understanding (SLU) is slot filling: representing the meaning of a spoken utterance using semantic entity labels.
P. Price, “Evaluation of spoken language systems: The ATIS domain,” in
1990
Earlier work this paper cites.
C. T. Hemphill, J. J. Godfrey, and G. R. Doddington, “The ATIS spoken language systems pilot corpus,” in
1990
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
V. Goel, H.-K. J. Kuo, S. Deligne, and C. Wu, “Language model estimation for optimizing end-to-end performance of a natural language call routing system,” in
2005
Earlier work this paper cites.
C. Raymond and G. Riccardi, “Generative and discriminative algorithms for spoken language understanding,” in
2007
Earlier work this paper cites.
S. Yaman, L. Deng, D. Yu, Y.-Y. Wang, and A. Acero, “An integrative and discriminative technique for spoken utterance classification,”
2008
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in
2009
Earlier work this paper cites.
G. Tur, D. Hakkani-Tür, and L. Heck, “What is left to be understood in ATIS?” in
2010
Earlier work this paper cites.
K. Veselý, M. Karafiát, and F. Grézl, “Convolutive bottleneck network features for LVCSR,” in
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
P. Xu and R. Sarikaya, “Convolutional neural network based triangular CRF for joint intent detection and slot filling,” in
2013
Earlier work this paper cites.
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus, “Regularization of neural networks using DropConnect,” in
2013
Earlier work this paper cites.
D. Guo, G. Tur, W.-t. Yih, and G. Zweig, “Joint semantic utterance classification and slot filling with recursive neural networks,” in
2014
Earlier work this paper cites.
G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L. Deng, D. Hakkani-Tur, X. He, L. Heck, G. Tur, D. Yu
2014
Cited alongside, same era.
B. Liu and I. Lane, “Recurrent neural network structured output prediction for spoken language understanding,” in
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in
2015
Cited alongside, same era.
J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” in
2015
Cited alongside, same era.
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio augmentation for speech recognition,” in
2015
Cited alongside, same era.
P. Haghani, A. Narayanan, M. Bacchiani, G. Chuang, N. Gaur, P. Moreno, R. Prabhavalkar, Z. Qu, and A. Waters, “From audio to semantics: Approaches to end-to-end spoken language understanding,” in
2018
Later among the works it cites.
D. Serdyuk, Y. Wang, C. Fuegen, A. Kumar, B. Liu, and Y. Bengio, “Towards end-to-end spoken language understanding,” in
2018
Later among the works it cites.
Y.-P. Chen, R. Price, and S. Bangalore, “Spoken language understanding without speech recognition,” in
2018
Later among the works it cites.
S. Ghannay, A. Caubrière, Y. Estève, N. Camelin, E. Simonnet, A. Laurent, and E. Morin, “End-to-end named entity and semantic concept extraction from speech,” in
2018
Later among the works it cites.
C.-W. Huang and Y.-N. Chen, “Adapting pretrained transformer to lattices for spoken language understanding,” in
2019
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2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
D. Hakkani-Tür, G. Tür, A. Celikyilmaz, Y.-N. Chen, J. Gao, L. Deng, and Y.-Y. Wang, “Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM,” in
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Cited alongside, same era.
Y. Qian, R. Ubale, V. Ramanaryanan, P. Lange, D. Suendermann-Oeft, K. Evanini, and E. Tsuprun, “Exploring ASR-free end-to-end modeling to improve spoken language understanding in a cloud-based dialog system,” in
2017
Cited alongside, same era.
D. Krueger, T. Maharaj, J. Kramár, M. Pezeshki, N. Ballas, N. R. Ke, A. Goyal, Y. Bengio, A. Courville, and C. Pal, “Zoneout: regularizing RNNs by randomly preserving hidden activations,” in
2017
Cited alongside, same era.
Later among the works it cites.
L. Lugosch, M. Ravanelli, P. Ignoto, V. S. Tomar, and Y. Bengio, “Speech model pre-training for end-to-end spoken language understanding,” in
2019
Later among the works it cites.
A. Caubrière, N. Tomashenko, A. Laurent, E. Morin, N. Camelin, and Y. Estève, “Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability,” in
2019
Later among the works it cites.
K. Audhkhasi, G. Saon, Z. Tüske, B. Kingsbury, and M. Picheny, “Forget a bit to learn better: Soft forgetting for CTC-based automatic speech recognition,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
D. S. Park, W. Chan, Y. Zhang, C. C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le, “SpecAugment: A simple data augmentation method for automatic speech recognition,” in
2019
Later among the works it cites.
G. Saon, Z. Tüske, K. Audhkhasi, and B. Kingsbury, “Sequence noise injected training for end-to-end speech recognition,” in
2019
Later among the works it cites.
Y. Huang, H.-K. Kuo, S. Thomas, Z. Kons, K. Audhkhasi, B. Kingsbury, R. Hoory, and M. Picheny, “Leveraging unpaired text data for training end-to-end speech-to-intent systems,” in
2020
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