  Recent works have empirically shown the effectiveness of data augmentation
(DA) in NLP tasks, especially for those suffering from data scarcity.
Intuitively, given the size of generated data, their diversity and quality are
crucial to the performance of targeted tasks. However, to the best of our
knowledge, most existing methods consider only either the diversity or the
quality of augmented data, thus cannot fully mine the potential of DA for NLP.
In this paper, we present an easy and plug-in data augmentation framework EPiDA
to support effective text classification. EPiDA employs two mechanisms:
relative entropy maximization (REM) and conditional entropy minimization (CEM)
to control data generation, where REM is designed to enhance the diversity of
augmented data while CEM is exploited to ensure their semantic consistency.
EPiDA can support efficient and continuous data generation for effective
classifier training. Extensive experiments show that EPiDA outperforms existing
SOTA methods in most cases, though not using any agent networks or pre-trained
generation networks, and it works well with various DA algorithms and
classification models. Code is available at
https://github.com/zhaominyiz/EPiDA.
