Title: Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems,
Abstract: Language understanding is a key component in a spoken dialogue system. In
this paper, we investigate how the language understanding module influences the
dialogue system performance by conducting a series of systematic experiments on
a task-oriented neural dialogue system in a reinforcement learning based
setting. The empirical study shows that among different types of language
understanding errors, slot-level errors can have more impact on the overall
performance of a dialogue system compared to intent-level errors. In addition,
our experiments demonstrate that the reinforcement learning based dialogue
system is able to learn when and what to confirm in order to achieve better
performance and greater robustness.