Dialog State Tracking with Reinforced Data Augmentation
This addresses data scarcity for dialog state tracking, an incremental improvement in a domain-specific task.
The paper tackles the problem of limited annotated training data for neural dialog state trackers by proposing a reinforcement learning-based data augmentation framework, which improves performance over state-of-the-art models on WoZ and MultiWoZ datasets, especially with limited data.
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. Specifically, we introduce a novel contextual bandit generator to learn fine-grained augmentation policies that can generate new effective instances by choosing suitable replacements for the specific context. Moreover, by alternately learning between the generator and the state tracker, we can keep refining the generative policies to generate more high-quality training data for neural state tracker. Experimental results on the WoZ and MultiWoZ (restaurant) datasets demonstrate that the proposed framework significantly improves the performance over the state-of-the-art models, especially with limited training data.