Few-shot multi-domain text intent classification with Dynamic Balance Domain Adaptation Meta-learning

User intents are ever-changing, which requires deep learning models to have the ability to classify unknown intents. Meta-learning aims to solve this problem by improving the model's generalization ability to unknown intent. However, learning on a small amount of text can easily lead to overfitting of the model. Domain adaptation can help us train a more robust model. However, most existing methods only focus on global feature alignment and ignore alignment in subdomains. Therefore, in this study, we first consider the case where the model can maintain robustness with a small amount of data and then explore and mine the higher quality transferable features. Based on these ideas, we propose Dynamic Balance Domain Adaptation Meta-learning (DBDAML), which adaptively learns higher quality transferable features in both the global domain and subdomains.(1) At the same time, we define a dynamic balance factor to enable DBDAML to dynamically focus on the global domain and subdomains. This allows the model to give different attention to different domain adaptations and prevents it from overfitting of a domain feature alignment. The dynamic balance factor is estimated by the contribution of different domain discriminators to the loss, which also makes it easy to calculate and accurate. Finally, we use the meta-learning framework to model the entire theoretical idea. Extensive experiments demonstrate that our approach achieves better performance than state-of-the-art baseline methods.