  Despite recent advancements in Machine Learning, many tasks still involve
working in low-data regimes which can make solving natural language problems
difficult. Recently, a number of text augmentation techniques have emerged in
the field of Natural Language Processing (NLP) which can enrich the training
data with new examples, though they are not without their caveats. For
instance, simple rule-based heuristic methods are effective, but lack variation
in semantic content and syntactic structure with respect to the original text.
On the other hand, more complex deep learning approaches can cause extreme
shifts in the intrinsic meaning of the text and introduce unwanted noise into
the training data. To more reliably control the quality of the augmented
examples, we introduce a state-of-the-art approach for Self-Controlled Text
Augmentation (STA). Our approach tightly controls the generation process by
introducing a self-checking procedure to ensure that generated examples retain
the semantic content of the original text. Experimental results on multiple
benchmarking datasets demonstrate that STA substantially outperforms existing
state-of-the-art techniques, whilst qualitative analysis reveals that the
generated examples are both lexically diverse and semantically reliable.
