Dialog State Tracking with Reinforced Data Augmentation

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 specific context. Moreover, by alternately learning between the generator and the state tracker, we can keep refining the generative policies to generate more highquality 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.

Authors

• Yichun Yin Huawei Noah’s Ark Lab, Shenzhen, Guangdong, China
• Lifeng Shang Huawei Noah’s Ark Lab, Shenzhen, Guangdong, China
• Xin Jiang School of Electronic Engineering, Xidian University, Xi’an, China
• Xiao Chen
• Qun Liu Huawei Noah's Ark Lab

Keywords

No keywords are indexed for this paper.