Online reinforcement learning for spoken dialogue systems: The story of a commercial deployment success

This paper shows how the convergence between design and monitoring tools, and the integration of a dedicated reinforcement learning can be complementary and offer a new design experience for Spoken Dialogue System (SDS) developers. Most industrial SDS developers use a graphical tool to implement the dialogue strategies. First, this article proposes to integrate dialogue logs into this design tool, so that it constitutes a monitoring tool as well, by revealing call flows and their associated Key Performance Indicators (KPI). Second, the SDS developer is opened the possibility of designing several alternatives and of visually comparing his de-sign choice performances. Third, reinforcement learning technique is integrated to automatically optimise the SDS choices. The design/monitoring tool helps the SDS developers to understand and analyse the user behaviour, with the assistance of the learning algorithm. The SDS developers can then confront the different KPI and control the further SDS choices by removing or adding alternatives.