Dialog State Tracking with Reinforced Data Augmentation

21 Aug 2019Yichun YinLifeng ShangXin JiangXiao ChenQun Liu

Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data. In this paper, we address this difficulty by proposing a reinforcement learning (RL) based framework for data augmentation that can generate high-quality data to improve the neural state tracker... (read more)

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