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Domain Transfer in Dialogue Systems without Turn-Level Supervision
[article]
2019
arXiv
pre-print
Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation. State-of-the-art DST models are typically trained in a supervised manner from manual annotations at the turn level. However, these annotations are costly to obtain, which makes it difficult to create accurate dialogue systems for new domains. To address these limitations, we propose a method, based on reinforcement learning, for
arXiv:1909.07101v1
fatcat:logglgkwfrd7hazddpbrs26lhm