G-DAug: Generative Data Augmentation for Commonsense Reasoning
Table 1: Results on the test sets of five commonsense benchmarks. RoBERTa (reported) is the result for the RoBERTa-large baseline reported in previous work. RoBERTa (ours) is our evaluation of RoBERTa-large baselines following our experimental setup. All G-DAug methods outperform the baseline methods, and G-DAug-Combo performs the best on the majority of the tasks (3/5) and achieves the highest average score.
['Method', 'CSQA (Acc)', 'WinoGrande (AUC)', 'Codah (Acc)', 'HellaSwag-2K (Acc)', 'SNLI-3K (Acc)', '[BOLD] Average']
The G-DAug methods perform better than the baselines: on average, all of the proposed selection algorithms achieve higher test performance than G-DAug-Rand. G-DAug-Combo performs the best on 3/5 tasks and obtains the highest average score 78.7%, a 1.6% absolute gain over the non-augmented baseline. Moreover, with G-DAug-Combo, we obtain a 5.0% absolute gain over previously published state of the art results on WinoGrande. , G-DAug-Combo outperforms the prior non-ensemble state-of-the-art Zhu et al. We also achieve a new state-of-the-art on Codah, where the previous best (BERT-based) score was 67.5% Chen et al. Backtranslation hurts performance: the non-augmented baseline uniformly outperforms Backtranslation, and the average score drops by 1% with Backtranslation.