User Simulator

material ID: usersimulator

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Guided Dialogue Policy Learning without Adversarial Learning in the Loop

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Publication: TIPSTER (2020)

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AbstractReinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy. However, these methods suffer from sparse and unstable reward signals returned by a …...

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Dynamic Dialogue Policy for Continual Reinforcement Learning

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AbstractContinual learning is one of the key components of human learning and a necessary requirement of artificial intelligence. As dialogue can potentially span infinitely many topics and tasks, a task-oriented …...

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