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

Is the User Enjoying the Conversation? A Case Study on the Impact on the Reward Function