Don’t Take the Easy Way Out:Ensemble Based Methods for Avoiding Known Dataset Biases
Table 2: Results on MNLI with different kinds of synthetic bias. The Acc columns show the accuracy on the out-of-domain test set, and the w/Bias columns show accuracy on the in-domain test. Unbiased Training is an upper bound constructed by training a model with the same randomized features that are used at test time.
['Debiasing Method', 'Indicator Acc.', 'Indicator w/Bias', 'Excluder Acc.', 'Excluder w/Bias', 'Dependent Acc.', 'Dependent w/Bias']
Results: All ensembling methods work well on the Indicator bias. The reweight method performs poorly on the Excluder bias, likely because the bias-only model assigns the correct class approximately 50% probability for almost all the training examples, making the weights mostly uniform. This illustrates a general weakness with reweighting methods: they require at least a small number of bias-free examples for the model to learn from.