Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual
Table 6: Complete results on MNLI Stress Test.
['[EMPTY]', '[EMPTY]', 'Negation  [ITALIC] E', 'Negation  [ITALIC] C', 'Negation  [ITALIC] N', 'Overlap  [ITALIC] E', 'Overlap  [ITALIC] C', 'Overlap  [ITALIC] N', 'Antonym  [ITALIC] E', 'Antonym  [ITALIC] C', 'Antonym  [ITALIC] N', 'Length  [ITALIC] E', 'Length  [ITALIC] C', 'Length  [ITALIC] N']
In addition to Overlap and Negation, which is intended to test dataset bias, we also include two tests that evaluate model performance on minority examples. Our debiased models have some improvement on Antonym, possibly as a by-product of focusing on challenge examples that cannot be solved by superficial cues. However, DRiFt did not improve performance on Length.