  Learning suitable and well-performing dialogue behaviour in statistical
spoken dialogue systems has been in the focus of research for many years. While
most work which is based on reinforcement learning employs an objective measure
like task success for modelling the reward signal, we use a reward based on
user satisfaction estimation. We propose a novel estimator and show that it
outperforms all previous estimators while learning temporal dependencies
implicitly. Furthermore, we apply this novel user satisfaction estimation model
live in simulated experiments where the satisfaction estimation model is
trained on one domain and applied in many other domains which cover a similar
task. We show that applying this model results in higher estimated
satisfaction, similar task success rates and a higher robustness to noise.
