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  2. paper.dvi

    mi.eng.cam.ac.uk/~mjfg/ragni_ICASSP11.pdf
    11 Mar 2011: 24, pp. 648–662, 2010. [4] I. Tsochantaridis, T. Joachims, T. Hofmann, and Y.
  3. 3 Jul 2018: shows that the CEDM learns to address a relationin up to 24.5% of all dialogues for r = 1.0. ... Computer Speech & Lan-guage, 24(2):150–174. Steve J. Young, Milica Gašić, Blaise Thomson, and Ja-son D.
  4. 20 Feb 2018: An advantage of this sparsification approach is that itenables non-positive definite kernel functions to be used in theapproximation, for example see [24]. ... It has already beenshown that active learning has the potential to lead to fasterlearning [24]
  5. 20 Feb 2018: Com-puter Speech & Language, 24(4):562–588, 2010. Y. Tokuda, T. Yoshimura, T. ... Computer Speech and Language,24(2):150–174, 2010.
  6. 20 Feb 2018: 564 B. Thomson, S. Young / Computer Speech and Language 24 (2010) 562–588. ... B. Thomson, S. Young / Computer Speech and Language 24 (2010) 562–588 565.
  7. crosseval_diff-reward2b.ps

    mi.eng.cam.ac.uk/~sjy/papers/kgjm10.pdf
    20 Feb 2018: Yu. 2009. The Hidden InformationState model: a practical framework for POMDPbased spoken dialogue management.ComputerSpeech and Language, 24(2):150–174.
  8. 20 Feb 2018: Thomson and Young2010] Blaise Thomson and SteveYoung. 2010. Bayesian update of dialogue state:A pomdp framework for spoken dialogue systems.Computer Speech and Language, 24:562–588.
  9. 20 Feb 2018: 24, no. 4, Oct. 2010. [16] S. J. Young, G. Evermann, M.
  10. 20 Feb 2018: In Proceedings of ACL, 2017. [24] Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Pei-Hao Su, DavidVandyke, Tsung-Hsien Wen, and Steve Young. ... Computer Speech & Language, 24(2):150–174, 2010. [53] Steve Young, Milica
  11. 20 Feb 2018: The feature set includes 24 spectralcoefficients, log F0 and 5 aperiodic component features.

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