Continuously Learning Neural Dialogue Management
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.
ArXiv