  We describe a two-step approach for dialogue management in task-oriented
spoken dialogue systems. A unified neural network framework is proposed to
enable the system to first learn by supervision from a set of dialogue data and
then continuously improve its behaviour via reinforcement learning, all using
gradient-based algorithms on one single model. The experiments demonstrate the
supervised model's effectiveness in the corpus-based evaluation, with user
simulation, and with paid human subjects. The use of reinforcement learning
further improves the model's performance in both interactive settings,
especially under higher-noise conditions.
