  Ensemble-based debiasing methods have been shown effective in mitigating the
reliance of classifiers on specific dataset bias, by exploiting the output of a
bias-only model to adjust the learning target. In this paper, we focus on the
bias-only model in these ensemble-based methods, which plays an important role
but has not gained much attention in the existing literature. Theoretically, we
prove that the debiasing performance can be damaged by inaccurate uncertainty
estimations of the bias-only model. Empirically, we show that existing
bias-only models fall short in producing accurate uncertainty estimations.
Motivated by these findings, we propose to conduct calibration on the bias-only
model, thus achieving a three-stage ensemble-based debiasing framework,
including bias modeling, model calibrating, and debiasing. Experimental results
on NLI and fact verification tasks show that our proposed three-stage debiasing
framework consistently outperforms the traditional two-stage one in
out-of-distribution accuracy.
