Distributional Data Augmentation Methods for Low Resource Language
Mosleh Mahamud Zed Lee Isak Samsten
Text augmentation is a technique for constructing synthetic data from an
under-resourced corpus to improve predictive performance. Synthetic data
generation is common in numerous domains. However, recently text augmentation
has emerged in natural language processing (NLP) to improve downstream tasks.
One of the current state-of-the-art text augmentation techniques is easy data
augmentation (EDA), which augments the training data by injecting and replacing
synonyms and randomly permuting sentences. One major obstacle with EDA is the
need for versatile and complete synonym dictionaries, which cannot be easily
found in low-resource languages. To improve the utility of EDA, we propose two
extensions, easy distributional data augmentation (EDDA) and type specific
similar word replacement (TSSR), which uses semantic word context information
and part-of-speech tags for word replacement and augmentation. In an extensive
empirical evaluation, we show the utility of the proposed methods, measured by
F1 score, on two representative datasets in Swedish as an example of a
low-resource language. With the proposed methods, we show that augmented data
improve classification performances in low-resource settings.