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Language understanding is a key component in a spoken dialogue system. Inthis paper, we investigate how the language understanding module influences thedialogue system performance by conducting a series of systematic experiments ona task-oriented neural dialogue system in a reinforcement learning basedsetting. The empirical study shows that among different types of languageunderstanding errors, slot-level errors can have more impact on the overallperformance of a dialogue system compared to intent-level errors. In addition,our experiments demonstrate that the reinforcement learning based dialoguesystem is able to learn when and what to confirm in order to achieve betterperformance and greater robustness.

Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems