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Optimisation for POMDP-based Spoken Dialogue Systems M. Gašić, F.…
mi.eng.cam.ac.uk/~sjy/papers/gjty12.pdf20 Feb 2018: To obtain a closed formsolution of (24), the policy π must be differentiable with respect to θ. ... 10. To lower the variance of the estimate of the gradient, a constant baseline, B, can beintroduced into (24) without introducing any bias [22]. -
Addressing Objects and Their Relations:The Conversational Entity…
mi.eng.cam.ac.uk/~sjy/papers/ubcr18.pdf3 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. -
Bayesian update of dialogue state: A POMDP framework for spoken…
mi.eng.cam.ac.uk/~sjy/papers/thyo10.pdf20 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. -
paper.dvi
mi.eng.cam.ac.uk/~mjfg/ragni_ICASSP11.pdf11 Mar 2011: 24, pp. 648–662, 2010. [4] I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. -
Phrase-based Statistical Language Generation usingGraphical Models…
mi.eng.cam.ac.uk/~sjy/papers/mgjk10.pdf20 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. -
crosseval_diff-reward2b.ps
mi.eng.cam.ac.uk/~sjy/papers/kgjm10.pdf20 Feb 2018: Yu. 2009. The Hidden InformationState model: a practical framework for POMDPbased spoken dialogue management.ComputerSpeech and Language, 24(2):150–174. -
DISCRIMINATIVE SPOKEN LANGUAGE UNDERSTANDINGUSING WORD CONFUSION…
mi.eng.cam.ac.uk/~sjy/papers/hgtt12.pdf20 Feb 2018: 24, no. 4, Oct. 2010. [16] S. J. Young, G. Evermann, M. -
A Benchmarking Environment for ReinforcementLearning Based Task…
mi.eng.cam.ac.uk/~sjy/papers/cbsm17.pdf20 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 -
From Discontinuous To Continuous F0 Modelling In HMM-based…
mi.eng.cam.ac.uk/~sjy/papers/yuty10.pdf20 Feb 2018: The feature set includes 24 spectralcoefficients, log F0 and 5 aperiodic component features. -
STRUCTURED DISCRIMINATIVE MODELS USING DEEP NEURAL-NETWORK FEATURES…
mi.eng.cam.ac.uk/~mjfg/vandalen_ASRU15.pdf12 Jul 2016: MPE— 7.15 11.06 14.37 24.54 16.79CML 6.95 11.00 14.29 24.39 16.68large-margin 7.02 10.92 14.16 24.28 ... Therefore the systems use graphemic lex-ica generated using an approach which is applicable to all Unicodecharacters [24].
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