  The impact of user satisfaction in policy learning task-oriented dialogue
systems has long been a subject of research interest. Most current models for
estimating the user satisfaction either (i) treat out-of-context short-texts,
such as product reviews, or (ii) rely on turn features instead of on
distributed semantic representations. In this work we adopt deep neural
networks that use distributed semantic representation learning for estimating
the user satisfaction in conversations. We evaluate the impact of modelling
context length in these networks. Moreover, we show that the proposed
hierarchical network outperforms state-of-the-art quality estimators.
Furthermore, we show that applying these networks to infer the reward function
in a Partial Observable Markov Decision Process (POMDP) yields to a great
improvement in the task success rate.
