Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog

IJCNLP 2019 Ryuichi TakanobuHanlin ZhuMinlie Huang

Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals... (read more)

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