  Data augmentation has proven widely effective in computer vision. In Natural
Language Processing (NLP) data augmentation remains an area of active research.
There is no widely accepted augmentation technique that works well across tasks
and model architectures. In this paper we explore data augmentation techniques
in the context of text classification using two social media datasets. We
explore popular varieties of data augmentation, starting with oversampling,
Easy Data Augmentation (Wei and Zou, 2019) and Back-Translation (Sennrich et
al., 2015). We also consider Greyscaling, a relatively unexplored data
augmentation technique that seeks to mitigate the intensity of adjectives in
examples. Finally, we consider a few-shot learning approach: Pattern-Exploiting
Training (PET) (Schick et al., 2020). For the experiments we use a BERT
transformer architecture. Results show that augmentation techniques provide
only minimal and inconsistent improvements. Synonym replacement provided
evidence of some performance improvement and adjective scales with Grayscaling
is an area where further exploration would be valuable. Few-shot learning
experiments show consistent improvement over supervised training, and seem very
promising when classes are easily separable but further exploration would be
valuable.
