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Task-Completion Dialogue Policy Learning

2 papers with code · Natural Language Processing
Subtask of Dialogue

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Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

ACL 2018 MiuLab/DDQ

During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience.

TASK-COMPLETION DIALOGUE POLICY LEARNING

Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

19 Nov 2018CrickWu/Swtich-DDQ

Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences.

ACTIVE LEARNING Q-LEARNING TASK-COMPLETION DIALOGUE POLICY LEARNING