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Despite recent advancements in Machine Learning, many tasks still involveworking in low-data regimes which can make solving natural language problemsdifficult. Recently, a number of text augmentation techniques have emerged inthe field of Natural Language Processing (NLP) which can enrich the trainingdata with new examples, though they are not without their caveats. Forinstance, simple rule-based heuristic methods are effective, but lack variationin semantic content and syntactic structure with respect to the original text.On the other hand, more complex deep learning approaches can cause extremeshifts in the intrinsic meaning of the text and introduce unwanted noise intothe training data. To more reliably control the quality of the augmentedexamples, we introduce a state-of-the-art approach for Self-Controlled TextAugmentation (STA). Our approach tightly controls the generation process byintroducing a self-checking procedure to ensure that generated examples retainthe semantic content of the original text. Experimental results on multiplebenchmarking datasets demonstrate that STA substantially outperforms existingstate-of-the-art techniques, whilst qualitative analysis reveals that thegenerated examples are both lexically diverse and semantically reliable.

STA: Self-controlled Text Augmentation for Improving Text Classifications