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Learning suitable and well-performing dialogue behaviour in statisticalspoken dialogue systems has been in the focus of research for many years. Whilemost work which is based on reinforcement learning employs an objective measurelike task success for modelling the reward signal, we use a reward based onuser satisfaction estimation. We propose a novel estimator and show that itoutperforms all previous estimators while learning temporal dependenciesimplicitly. Furthermore, we apply this novel user satisfaction estimation modellive in simulated experiments where the satisfaction estimation model istrained on one domain and applied in many other domains which cover a similartask. We show that applying this model results in higher estimatedsatisfaction, similar task success rates and a higher robustness to noise.

Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning