Improving short text classification through global augmentation methods
This work provides practical insights for practitioners and researchers on choosing augmentation methods for classification tasks, though it is incremental in nature.
The study investigated text augmentation methods for short text classification, finding that Word2vec-based augmentation is effective without formal synonym models and that mixup improves performance and reduces overfitting in deep learning models.
We study the effect of different approaches to text augmentation. To do this we use 3 datasets that include social media and formal text in the form of news articles. Our goal is to provide insights for practitioners and researchers on making choices for augmentation for classification use cases. We observe that Word2vec-based augmentation is a viable option when one does not have access to a formal synonym model (like WordNet-based augmentation). The use of \emph{mixup} further improves performance of all text based augmentations and reduces the effects of overfitting on a tested deep learning model. Round-trip translation with a translation service proves to be harder to use due to cost and as such is less accessible for both normal and low resource use-cases.