Towards Robustifying NLI Models Against Lexical Dataset Biases
Table 2: The performance for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data.
['[BOLD] Train/Test', '[BOLD] MNLI  [BOLD] Acc', '[BOLD] Bal  [BOLD] Acc', '[BOLD] Bal  [BOLD] Acc_hr', '[BOLD] Stress*  [BOLD] Acc', '[BOLD] Stress*  [BOLD] Acc_hr']
Since we observe similar performance for CWB and WOB, we leave the results for WOB in Appendix. On every dataset, there’s a significant gap between Acc and Acc_hr, showing the baseline has both strong CWB bias and strong WOB bias. For the data augmentation/enhancement experiments, we report results after adding 500/20,000/50,000 additional samples. We demonstrate the effect of adding a small portion of data for the 500 case and the limitation of this method using the 20,000 and 50,000 cases. We use “+origin” to denote the results from data enhancement using the original dataset and use “+synthetic” to denote the results from data augmentation by generating new synthetic data similar to NLI stress tests.