  Deep learning is providing very positive results in areas related to
conversational interfaces, such as speech recognition, but its potential
benefit for dialog management has still not been fully studied. In this paper,
we perform an assessment of different configurations for deep-learned dialog
management with three dialog corpora from different application domains and
varying in size, dimensionality and possible system responses. Our results have
allowed us to identify several aspects that can have an impact on accuracy,
including the approaches used for feature extraction, input representation,
context consideration and the hyper-parameters of the deep neural networks
employed.
