Hybridisation of expertise and reinforcement learning in dialogue systems

This paper addresses the problem of introducing learning capabilities in industrial handcrafted automata-based Spoken Dialogue Systems, in order to help the developer to cope with his dialogue strategies design tasks. While classical reinforcement learning algorithms position their learning at the dialogue move level, the fundamental idea behind our approach is to learn at a finer internal decision level (which question, which words, which prosody, . . . ). These internal decisions are made on the basis of different (distinct or overlapping) knowledge. This paper proposes a novel reinforcement learning algorithm that can be used to make a data-driven optimisation of such handcrafted systems. An experiment shows that the convergence can be up to 20 times faster than with Q-Learning.