A methodology for turn-taking capabilities enhancement in Spoken Dialogue Systems using Reinforcement Learning. (January 2018) - Khouzaimi, Hatim; Laroche, Romain; Lefèvre, Fabrice
Highlights: We propose a new methodology to improve turn-taking capabilities in a Spoken Dialogue System using Reinforcement Learning. We describe a new incremental Dialogue System Architecture and a new incremental dialogue simulator. We train a Reinforcement Learning policy with the simulator. We show that it outperforms the handcrafted and the non-incremental baseline strategies. We validate these results in a live study with real users. Abstract: This article introduces a new methodology to enhance an existing traditional Spoken Dialogue System (SDS) with optimal turn-taking capabilities in order to increase dialogue efficiency. A new approach for transforming the traditional dialogue architecture into an incremental one at a low cost is presented: a new turn-taking decision module called the Scheduler is inserted between the Client and the Service. It is responsible for handling turn-taking decisions. Then, a User Simulator which is able to interact with the system using this new architecture has been implemented and used to train a new Reinforcement Learning turn-taking strategy. Compared to a non-incremental and a handcrafted incremental baselines, it is shown to perform better in simulation and in a real live experiment.