End-to-End Bias Mitigation by Modelling Biases in Corpora (1909.06321v3)

Abstract: Several recent studies have shown that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models that fail to generalize to out-of-domain datasets and are likely to perform poorly in real-world scenarios. We propose two learning strategies to train neural models, which are more robust to such biases and transfer better to out-of-domain datasets. The biases are specified in terms of one or more bias-only models, which learn to leverage the dataset biases. During training, the bias-only models' predictions are used to adjust the loss of the base model to reduce its reliance on biases by down-weighting the biased examples and focusing the training on the hard examples. We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data. Results show that our debiasing methods greatly improve robustness in all settings and better transfer to other textual entailment datasets. Our code and data are publicly available in \url{https://github.com/rabeehk/robust-nli}.

A Comprehensive Examination of Bias Mitigation in Neural Models

The paper "End-to-End Bias Mitigation by Modelling Biases in Corpora" presents an insightful exploration into the domain of debiasing techniques for neural models, particularly those involved in natural language understanding (NLU). It addresses a significant challenge in machine learning: the inclination of models to exploit dataset biases, leading to suboptimal generalization when applied to out-of-domain datasets. The work introduces two novel debiasing strategies—Product of Experts (PoE) and Debiased Focal Loss (DFL)—that aim to build models less reliant on these biases.

Core Methods

The authors propose two main strategies to mitigate biases in training datasets:

1. Product of Experts (PoE): This method integrates both a base model and a bias-only model, leveraging an ensemble approach that combines their predictions to strategically reduce the influence of biases. Specifically, it computes the training loss over the ensemble, diminishing the loss for examples correctly classified by the bias-only model. This encourages the base model to focus on more challenging, less biased instances.
2. Debiased Focal Loss (DFL): Adapted from traditional focal loss, DFL utilizes predictions from a bias-only model to modulate the error weighting in the base model. This approach explicitly down-weights loss for examples where the bias-only model performs well, thus emphasizing hard examples. The method parameterizes the degree of down-weighting, allowing for flexibility based on task-specific requirements.

Both methods provide tailored solutions to specific bias patterns and demonstrate increased robustness across various NLU benchmarks.

Empirical Evaluation

The research extensively tests these strategies on diverse NLI and fact verification datasets such as SNLI, MNLI, HANS, and FEVER. The evaluations involve:

• Performance Improvement: The proposed methods consistently enhance model performance on unbiased and out-of-domain datasets. Notable improvements include a 9.8-point increase on the FEVER symmetric test set and a 7.4-point gain on the HANS dataset, indicating the effectiveness of these techniques in enhancing model generalization.
• Transfer Learning: The research also evaluates the transferability of debiased models across 12 different NLI datasets. The findings demonstrate enhanced generalization capabilities, with models trained using PoE and DFL outperforming baselines on most datasets.

These results highlight the potential of debiasing methods to improve cross-domain applicability of NLU models, a critical factor for real-world deployment.

Implications and Future Prospects

This paper makes several pivotal contributions to the field:

• Robust NLU Models: By presenting effective debiasing mechanisms, the paper supports the development of more robust NLU models that can withstand shifts in data domain and reduce reliance on superficial statistical cues.
• Versatility and Simplicity: The proposed methods are model-agnostic and relatively straightforward to implement, enhancing their accessibility for various applications across different domains.
• Foundation for Further Research: While the current approaches require predefined identification of bias patterns, the framework sets a foundation for further exploration into automated bias detection and mitigation strategies.

In conclusion, this research advances the field of bias mitigation in neural networks, offering valuable insights and tools for developing models that are both robust and generalizable. Future work may aim to refine these techniques, potentially incorporating automatic bias detection capabilities, thereby broadening their applicability and efficacy.