Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual
Table 3: Accuracy of models trained by MLE, DRiFt, and Rm with different biased models. Training and test examples are from the same dataset. Intensity of the red highlights corresponds to absolute drop in accuracy with respect to the MLE baseline. Rm significantly hurts in-distribution performance. DRiFt maintains reasonable performance.
['[EMPTY]', 'SNLI BERT', 'SNLI DA', 'SNLI ESIM', 'MNLI BERT', 'MNLI DA', 'MNLI ESIM']
Baseline results from our implementations are comparable to prior reported performance (row “MLE”). Debiased models trained by DRiFt show some degradation on in-distribution data, especially for the less powerful DA and ESIM models. The accuracy drop is expected due to two reasons. First, DRiFt assumes distribution shift thus does not optimize performance on the training distribution P. Second, the effective training data size is reduced by negative gradients on potentially biased examples; this effect is exaggerated by Rm, which shows significant in-distribution degradation. Similar trade-off between in-distribution accuracy and robustness on out-of-distribution data has also been observed in adversarial training zhang2019theoretically; tsipras2019robustness.