Temporal supervised learning for inferring a dialog policy from example conversations

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Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management (2017, background): “Secondly, there will often be a large number of dialogue states which are not covered by the training data (Henderson et al., 2008; Li et al., 2014).”
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning (2016, background): “The foremost challenge is that a task-oriented system must learn a strategic dialog policy that can achieve the goal of a given task which is beyond the ability of standard supervised learning (Li et al., 2014).”
Neural Approaches to Conversational AI (2018, unknown): “ 2000; Rieser and Lemon, 2008; Rieser et al., 2010; El Asri et al., 2012) which can better correlate with user satisfaction (Rieser and Lemon, 2011), or is more consistent with expert demonstrations (Li et al., 2014). Su et al. (2015) proposed to rate dialogue success with two neural network models,”