Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
Table 1: POS tagging accuracies on UDv2.1 test sets. Best scores are shown with bold. Org: Original. p: operation probability. Imp%: Improvement over original (Org) by the best model trained with the augmented data.
['[BOLD] #Tokens', '[BOLD] Lang', '[BOLD] Type', '[BOLD] Org', '[BOLD] crop  [ITALIC] p=0.3', '[BOLD] crop  [ITALIC] p=0.7', '[BOLD] crop  [ITALIC] p=1', '[BOLD] rotate  [ITALIC] p=0.3', '[BOLD] rotate  [ITALIC] p=0.7', '[BOLD] rotate  [ITALIC] p=1', '[ITALIC] Imp%']
Average of multiple runs for 20 languages are given in Fig. Here, Org column refers to our baseline with non-augmented, original training set, where Imp% is the improvement over the baseline by the best crop/flip model for that language. It is evident that, with some minor exceptions, all languages have benefited from a type of augmentation. Our result on both languages show that both operations reduced the generalization error surprisingly well in the lack of training data.