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The partially observable Markov decision process (POMDP) has been proposed as a dialogue model that enables automatic improvement of the dialogue policy and robustness to speech understanding errors. It requires, however, a large number of dialogues to train the dialogue policy. Gaussian processes (GP) have recently been applied to POMDP dialogue management optimisation showing an ability to substantially increase the speed of learning. Here, we investigate this further using the Bayesiandoi:10.1109/slt.2012.6424165 dblp:conf/slt/GasicHTTY12 fatcat:y5nryd22kjfv5ih24ullxtv4hy