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A HIERARCHICAL ATTENTION BASED MODEL FOR OFF-TOPIC SPONTANEOUSSPOKEN…
mi.eng.cam.ac.uk/~mjfg/ALTA/publications/ASRU2017/HierarchicalAttentionBased/hierarchical-attention-based.pdf24 Jan 2018: The HATMalso contains an additional 200-dimensional BiLSTM prompt-searchencoder. The ATM was trained for 5 epochs with the Adam op-timizer [19], an exponentially decaying learning rate with an -
k-Nearest Neighbor Monte-Carlo Control Algorithmfor POMDP-based…
mi.eng.cam.ac.uk/~sjy/papers/lgjk09.pdf20 Feb 2018: In Section 3, the grid-based ap-. proach to policy optimisation is introduced followedby a presentation of the k-nn Monte-Carlo policy op-timization in Section 4, along with an ... 5 ConclusionIn this paper, an extension to a grid-based policy -
POLICY COMMITTEE FOR ADAPTATION IN MULTI-DOMAIN SPOKEN…
mi.eng.cam.ac.uk/~sjy/papers/gmsv15.pdf20 Feb 2018: 5]. Here, we address the problem ofdecision-making. Moving from a limited domain dialogue system that op-erates on a relatively modest ontology to an open domain. ... 5. EXPERIMENTAL SET-UP. In order to examine the ability of the proposed method to -
The Effect of Cognitive Load on a Statistical Dialogue ...
mi.eng.cam.ac.uk/~sjy/papers/gtht12.pdf20 Feb 2018: 4.4 Conversational patterns. Given that the subjects felt the change of cognitiveload when they were talking to the system and op-erating the car simulator at the same time, we -
crosseval_diff-reward2b.ps
mi.eng.cam.ac.uk/~sjy/papers/kgjm10.pdf20 Feb 2018: The op-tions for each random decision point are reason-able in the context in which it is encountered, buta uniform distribution of outcomes might not re-flect real user behaviour. ... Many of the decisions involvedare deterministic, allowing only one -
Learning Domain-Independent Dialogue Policies via…
mi.eng.cam.ac.uk/~sjy/papers/wsws15.pdf20 Feb 2018: The ex-perimental results show that the policy op-timised in a restaurant search domain us-ing our domain-independent representa-tions can be deployed to a laptop sale do-main, -
Incremental on-line adaptation of POMDP-based dialogue managers…
mi.eng.cam.ac.uk/~sjy/papers/gktb14.pdf20 Feb 2018: the Q-function estimatewe expect during the process of learning and H is a linear op-erator that captures the reward lookahead from the Q-function(see Eq. -
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. ...
mi.eng.cam.ac.uk/~sjy/papers/scyo09.pdf20 Feb 2018: increases. Pop op-erations are then performed where possible, the tree is prunedand identical nodes are joined so that the number stays constantor decreases. ... Error bars indicate 99% con-fidence intervals. This demonstrates the competitiveness of the -
Sample-efficient Actor-Critic Reinforcement Learningwith Supervised…
mi.eng.cam.ac.uk/~sjy/papers/sbug17.pdf20 Feb 2018: A comparison between the three op-tions is included in the experimental evaluation. ... whilst suffering initially.We hypothesise that the optimised SL pre-trainedparameters distributed very differently to the op-timal A2C ER parameters. -
robust.dvi
mi.eng.cam.ac.uk/~sjy/papers/heyo04.pdf20 Feb 2018: 4.2 Log-Linear Interpolation. Log-linear interpolation has been applied to languagemodel adaptation and has been shown to be equivalentto a constrained minimum Kullback-Leibler distance op-timisation problem(Klakow,
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