You can read these blog posts to get an overview of the approaches.
Neural NLP systems achieve high scores in the presence of sizable training dataset.
We present EDA: easy data augmentation techniques for boosting performance on text classification tasks.
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels.
In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system.
We examine the effect of data augmentation for training of language models for speech recognition.