Contextual Augmentation:Data Augmentation by Words with Paradigmatic Relations
Table 1: Accuracies of the models for various benchmarks. The accuracies are averaged over eight models trained from different seeds.
['[BOLD] Models', '[BOLD] STT5', '[BOLD] STT2', '[BOLD] Subj', '[BOLD] MPQA', '[BOLD] RT', '[BOLD] TREC', '[BOLD] Avg.']
The results show that our contextual augmentation improves the model performances for various datasets from different domains more significantly than the prior synonym-based augmentation does. Furthermore, our label-conditional architecture boosted the performances on average and achieved the best accuracies. Our methods are effective even for datasets with more than two types of labels, SST5 and TREC.