A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems

Understand

User simulation is essential for generating enough data to train a statistical spoken dialogue system.

• Previous models for user simulation suffer from several drawbacks, such as the inability to take dialogue history into account, the need of rigid structure to ensure coherent user behaviour, heavy dependence on a specific domain, the inability to output several user intentions during one dialogue turn, or the requirement of a summarized action space for tractability.
• This paper introduces a data-driven user simulator based on an encoder-decoder recurrent neural network.
• The model takes as input a sequence of dialogue contexts and outputs a sequence of dialogue acts corresponding to user intentions.