Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation

We investigate the effect of data augmentation on low-resource morphological segmentation. We compare two settings: the pure low-resource one, when only 100 annotated word forms are available, and the augmented one, where we use the original training set and 1000 unlabeled word forms to generate 1000 artificial inflected forms. Evaluating on Sigmorphon 2018 dataset, we observe that using the best among these two models reduces the error rate of state-of-the-art model by 6%, while for our baseline model the error reduction is 17%