Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual
Table 2: Accuracy of biased classifiers on SNLI test set and MNLI development set. All exceeds the majority-class baseline by a large margin, signaling dataset bias.
['Dataset', 'majority', 'Hypo', 'CBOW', 'Hand']
We first evaluate the models’ in-distribution performance where they are trained and evaluated on splits from the same dataset. All exceeds the majority-class baseline by a large margin, indicating that a majority of examples can be solved by superficial cues.