Improving Bias Mitigation through Bias Experts in Natural Language Understanding (2312.03577v1)

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.

Improving Bias Mitigation through Bias Experts in Natural Language Understanding

Paper Overview

The paper "Improving Bias Mitigation through Bias Experts in Natural Language Understanding" focuses on addressing the challenge of dataset biases in deep neural networks, particularly in Natural Language Understanding (NLU) tasks. Traditional debiasing methods often involve down-weighting biased examples identified by auxiliary models trained with explicit bias labels. However, acquiring bias labels for datasets is labor-intensive and costly. This paper proposes a novel debiasing framework that introduces "bias experts"—binary classifiers trained via the One-vs-Rest (OvR) approach—between the auxiliary model and the main model to enhance bias detection and mitigation.

Key Contributions

1. Critique of Existing Methods: The authors articulate the limitations of the multi-class learning objective typically used in previous debiasing methods. They argue that this approach can inadvertently inhibit the auxiliary model's ability to identify biases due to its regularization effects and the competitive nature across classes.
2. Introduction of Bias Experts: To overcome these issues, the paper proposes bias experts that focus on identifying biased examples within specific classes using binary learning objectives. By employing the OvR approach, each bias expert individually learns the bias attributes of a target class without interference from other classes.
3. Experimental Validation: The proposed framework's effectiveness is validated across various challenging datasets, including MNLI, FEVER, and QQP, which are well-known benchmarks in NLU tasks. The results show significant improvements in out-of-distribution performance compared to state-of-the-art methods.

Detailed Methodology

One-vs-Rest Approach

The OvR approach transforms a multi-class classification problem into multiple binary classification tasks. For instance, in the MNLI dataset, which includes classes like "contradiction," "entailment," and "neutral," the OvR approach creates binary classification sets: "Contradiction vs Non-Contradiction," "Entailment vs Non-Entailment," and "Neutral vs Non-Neutral." This strategy facilitates each bias expert to learn the bias attributes specific to its target class.

Training Bias Experts

Each bias expert is trained with a weighted loss that emphasizes bias-conflicting examples and down-weights biased examples in the non-target classes. This arrangement ensures that each expert is particularly sensitive to the biases pertinent to its specific class. The loss function for training these experts is formulated to discourage learning biases from non-target classes, thereby honing the expert's focus on its assigned bias type.

Training the Debiased Model

The debiased model is trained using a Product-of-Experts (PoE) method, which combines the outputs of the main model and the bias experts. The ensemble loss down-weights examples correctly identified by the bias experts, thus discouraging the main model from exploiting these biases.

Experimental Results

The authors report extensive experimental results demonstrating the proposed framework's superior performance in reducing bias across various datasets:

• MNLI and HANS: The framework achieves an accuracy improvement of 1.4% on the HANS dataset over the best-performing baseline.
• FEVER and FEVER Symmetric: The method outperforms baselines with a 2.1% improvement on the FEVER Symmetric dataset.
• QQP and PAWS: It also shows a 0.7% gain on the PAWS dataset compared to state-of-the-art methods.

Moreover, the framework shows a smaller gap between in-distribution and out-of-distribution performances, indicating more robust generalization.

Implications and Future Directions

The proposed framework has significant implications for both practical applications and theoretical advancements in AI:

• Practical Applications: The ability to mitigate biases more effectively can lead to more reliable AI systems in real-world scenarios, including natural language processing, image recognition, and other domains.
• Theoretical Advancements: This work contributes to the understanding of how different learning objectives impact bias accumulation in models, opening avenues for further research on bias mitigation techniques.

Future research can explore ways to alleviate the framework's potential memory usage issues in datasets with a large number of classes and investigate hyperparameter tuning methodologies for bias mitigation without relying on out-of-distribution validation sets.

Conclusion

This paper puts forward a compelling framework for improving bias mitigation in NLU by introducing bias experts trained through the OvR approach. The empirical results validate the effectiveness and robustness of this approach across multiple datasets, making a substantial contribution to the field of bias mitigation in machine learning.