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Despite its notable success in adversarial learning approaches tomulti-domain task-oriented dialog system, training the dialog policy viaadversarial inverse reinforcement learning often fails to balance theperformance of the policy generator and reward estimator. During optimization,the reward estimator often overwhelms the policy generator and producesexcessively uninformative gradients. We proposes the Variational Rewardestimator Bottleneck (VRB), which is an effective regularization method thataims to constrain unproductive information flows between inputs and the rewardestimator. The VRB focuses on capturing discriminative features, by exploitinginformation bottleneck on mutual information. Empirical results on amulti-domain task-oriented dialog dataset demonstrate that the VRBsignificantly outperforms previous methods.

Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog