Improving Bias Mitigation through Bias Experts in Natural Language
  Understanding
Eojin Jeon Mingyu Lee Juhyeong Park Yeachan Kim Wing-Lam Mok SangKeun Lee
Biases in the dataset often enable the model to achieve high performance on
in-distribution data, while poorly performing on out-of-distribution data. To
mitigate the detrimental effect of the bias on the networks, previous works
have proposed debiasing methods that down-weight the biased examples identified
by an auxiliary model, which is trained with explicit bias labels. However,
finding a type of bias in datasets is a costly process. Therefore, recent
studies have attempted to make the auxiliary model biased without the guidance
(or annotation) of bias labels, by constraining the model's training
environment or the capability of the model itself. Despite the promising
debiasing results of recent works, the multi-class learning objective, which
has been naively used to train the auxiliary model, may harm the bias
mitigation effect due to its regularization effect and competitive nature
across classes. As an alternative, we propose a new debiasing framework that
introduces binary classifiers between the auxiliary model and the main model,
coined bias experts. Specifically, each bias expert is trained on a binary
classification task derived from the multi-class classification task via the
One-vs-Rest approach. Experimental results demonstrate that our proposed
strategy improves the bias identification ability of the auxiliary model.
Consequently, our debiased model consistently outperforms the state-of-the-art
on various challenge datasets.