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For task-oriented dialog systems, training a Reinforcement Learning (RL)based Dialog Management module suffers from low sample efficiency and slowconvergence speed due to the sparse rewards in RL.To solve this problem, manystrategies have been proposed to give proper rewards when training RL, buttheir rewards lack interpretability and cannot accurately estimate thedistribution of state-action pairs in real dialogs. In this paper, we propose amulti-level reward modeling approach that factorizes a reward into athree-level hierarchy: domain, act, and slot. Based on inverse adversarialreinforcement learning, our designed reward model can provide more accurate andexplainable reward signals for state-action pairs.Extensive evaluations showthat our approach can be applied to a wide range of reinforcementlearning-based dialog systems and significantly improves both the performanceand the speed of convergence.

Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management