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Text augmentation is a technique for constructing synthetic data from anunder-resourced corpus to improve predictive performance. Synthetic datageneration is common in numerous domains. However, recently text augmentationhas emerged in natural language processing (NLP) to improve downstream tasks.One of the current state-of-the-art text augmentation techniques is easy dataaugmentation (EDA), which augments the training data by injecting and replacingsynonyms and randomly permuting sentences. One major obstacle with EDA is theneed for versatile and complete synonym dictionaries, which cannot be easilyfound in low-resource languages. To improve the utility of EDA, we propose twoextensions, easy distributional data augmentation (EDDA) and type specificsimilar word replacement (TSSR), which uses semantic word context informationand part-of-speech tags for word replacement and augmentation. In an extensiveempirical evaluation, we show the utility of the proposed methods, measured byF1 score, on two representative datasets in Swedish as an example of alow-resource language. With the proposed methods, we show that augmented dataimprove classification performances in low-resource settings.

Distributional Data Augmentation Methods for Low Resource Language