Improving Bias Mitigation through Bias Experts in Natural Language Understanding (2023)

Abstract: 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.

## introduction deep neural networks achieve state-of-the-art performances on a variety of multi-class classification tasks, including image classification @xcite and natural language inference @xcite . however, they are often biased towards spurious correlations between inputs and labels, which work well on a specific data distribution @xcite @xcite . for instance, in natural language inference (nli) tasks, neural networks are more likely to predict examples containing negation words as the contradiction class @xcite . ultimately, this unintended usage of bias results in poor model performance on out-of-distribution data or bias-conflicting foot_0 examples, where spurious correlations do not exist. therefore, it is important to develop debiasing methods. previous works have addressed the issue by guiding the main model to down-weight biased examples during training, which are identified by auxiliary models. the auxiliary models are models that intentionally learn bias features by utilizing human supervision @xcite @xcite or prior knowledge about dataset biases @xcite . however, acquiring human supervision or prior knowledge about biases in myriad datasets is a dauntingly laborious and costly process. recent studies have therefore shifted towards training the auxiliary models without human guidance (or bias labels). these studies attempt to make the auxiliary model biased by limiting the model capacity @xcite , training for fewer epochs @xcite , restricting accessible data @xcite , or using gce loss @xcite . despite promising results, they still have room for further improvement. specifically, the multiclass learning objective (e.g., softmax crossentropy), which previous works have naively used to train the auxiliary model, may harm the bias identification ability of the model from a few aspects: a regularization effect and a competitive nature across classes. @xcite have demonstrated that the multi-class learning objective has a regularization effect that prevents the model from overfitting on the training data. this is generally desirable for training the model, but not for training the auxiliary model that needs to be overfitted to bias features. since the softmax normalization results in the sum of all the class logits equal to one, lowering the prediction confidence in one class necessarily increases the prediction confidence in the other classes @xcite . it can easily lead to overconfident predictions and may cause the auxiliary model to misidentify unbiased examples as biased examples. to address the aforementioned problem, we propose a novel debiasing framework that introduces bias experts between the auxiliary model and the main model. as shown in figure 1 , the bias expert is a binary classifier that identifies biased examples within a specific class rather than multiple classes. in practice, we first cast the multi-class classification task into multiple individual binary classification tasks via the one-vs-rest approach @xcite , which encourages the model to individually learn the bias attributes of the target class without the influence of other classes. in addition, our framework motivated by @xcite highlights biased examples of target classes that the auxiliary model deems "easy", allowing bias experts to focus more on learning the bias attributes of the class in charge. finally, we train the main model by reweighting the loss of the exam-ples with bias experts. we validate the effectiveness of the proposed framework on various challenging datasets. experiment results show that our framework significantly improves performance on all datasets. the contributions of this paper are as follows: ## motivation in this section, we describe our empirical observations on the existing debiasing approach. these observations serve as intuitions for designing and understanding our bias experts. we first provide a description of the experimental setup in section 2.1. then we provide our empirical observations in section 2.2. ## proposed method based on our observation in section 2, we propose a novel debiasing framework, introducing bias experts between the auxiliary model and the main model to improve bias mitigation methods. framework is illustrated conceptually in figure 3 . ## conclusion we have proposed a novel framework that introduces a binary classifier between the main model and the auxiliary model, called bias experts, for improving the bias mitigation method. the main intuition of this work is to mitigate the incorrect bias identification caused by the naive application of the multi-class learning objective to train the auxiliary model. we train our bias experts via the one-vs-rest approach, pushing each bias expert to focus more on the biased examples in its target class. through various experiments and ablation studies, we demonstrate our framework effectively alleviates the aforementioned problem and improves existing bias mitigation methods.