Towards deeper understanding: Deep convex networks for semantic utterance classification

@article{Tr2012TowardsDU,
  title={Towards deeper understanding: Deep convex networks for semantic utterance classification},
  author={G{\"o}khan T{\"u}r and Li Deng and Dilek Z. Hakkani-T{\"u}r and Xiaodong He},
  journal={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2012},
  pages={5045-5048}
}
Following the recent advances in deep learning techniques, in this paper, we present the application of special type of deep architecture - deep convex networks (DCNs) - for semantic utterance classification (SUC). DCNs are shown to have several advantages over deep belief networks (DBNs) including classification accuracy and training scalability. However, adoption of DCNs for SUC comes with non-trivial issues. Specifically, SUC has an extremely sparse input feature space encompassing a very… CONTINUE READING
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Intent Determination and Spoken Utterance Classification, Chpater 3 in Book: Spoken Language Understanding

  • G. Tur, L. Deng
  • 2011
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