  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. The dialogue contexts include information
about the machine acts and the status of the user goal. We show on the Dialogue
State Tracking Challenge 2 (DSTC2) dataset that the sequence-to-sequence model
outperforms an agenda-based simulator and an n-gram simulator, according to
F-score. Furthermore, we show how this model can be used on the original action
space and thereby models user behaviour with finer granularity.
