G-DAug: Generative Data Augmentation for Commonsense Reasoning
Table 2: Results on WordNet-based synonym replacement sets. Metrics are the same as Table 2. G-DAug-Diversity and G-DAug-Combo achieve the highest average score.
['Method', 'CSQA', 'WinoGrande', 'Codah', 'Hellaswag-2K', 'SNLI-3K', '[BOLD] Average']
For Codah and HellaSwag-2K, we perturb test folds/sets, as the labels are available. Performing synonym replacement does not guarantee that the perturbed examples are still correct, due to e.g. polysemy. However, assuming the models perform similarly on invalid questions, higher accuracy on synonym-replaced questions may suggest better performance on the valid perturbations. G-DAug outperforms the baselines, with G-DAug-Combo and G-DAug-Diversity obtaining the best average performance. This suggests that augmenting with diverse questions is helpful for model robustness.