Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation (2005.00965v1)

Abstract: Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models. Some commonly adopted debiasing approaches, including the seminal Hard Debias algorithm, apply post-processing procedures that project pre-trained word embeddings into a subspace orthogonal to an inferred gender subspace. We discover that semantic-agnostic corpus regularities such as word frequency captured by the word embeddings negatively impact the performance of these algorithms. We propose a simple but effective technique, Double Hard Debias, which purifies the word embeddings against such corpus regularities prior to inferring and removing the gender subspace. Experiments on three bias mitigation benchmarks show that our approach preserves the distributional semantics of the pre-trained word embeddings while reducing gender bias to a significantly larger degree than prior approaches.

Analysis of the Double-Hard Debias Method for Word Embeddings

The paper entitled "Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation" presents a novel approach to mitigate gender bias in pre-trained word embeddings. Word embeddings are foundational to NLP, yet they can perpetuate gender biases present in the corpora on which they are trained. This paper introduces Double-Hard Debias, a method building upon the established Hard Debias approach, that aims to improve the extent to which gender bias is reduced while maintaining the utility of word embeddings for NLP tasks.

Overview of Current Approaches

Current efforts to address gender bias in word embeddings can generally be categorized into methods that modify the training process and post-processing adjustments. Training-based methods, while effective, are computationally intensive and may necessitate retraining embeddings used in numerous existing models. Post-processing approaches, such as Hard Debias, identify and remove components in embeddings associated with gender direction. These methods are advantageous due to their computational efficiency and applicability to existing models. However, their effectiveness can be limited, as biases can sometimes still be inferred from the geometry of the embeddings.

Motivation for Double-Hard Debias

The paper identifies a significant factor overlooked by current methods—the impact of word frequency statistics on the gender direction inferred in embeddings. Through empirical analysis, the authors show that changes in the frequency of gender-related words can dramatically alter the gender direction captured by embeddings. Therefore, addressing these frequency effects should improve gender debiasing performance.

Double-Hard Debias Methodology

Double-Hard Debias enhances the Hard Debias method by introducing an intermediary step that removes frequency-induced distortions before identifying and eliminating gender bias. The authors propose projecting embeddings onto a subspace that neutralizes word frequency statistics, identified through principal component analysis (PCA) of the embeddings. Subsequent application of Hard Debias to these modified embeddings then yields more effectively debiased results.

Experimental Results

The paper evaluates the method using both intrinsic and extrinsic metrics. Intrinsically, Double-Hard Debias achieves lower bias scores in the Word Embeddings Association Test (WEAT) and neighborhood metrics, indicating a significant reduction in gender bias compared to existing methods. Extrinsically, the proposed method maintains performance on NLP tasks such as coreference resolution, word analogy, and concept categorization. It particularly excels in reducing the disparity in performance between pro-stereotype and anti-stereotype subsets in the WinoBias dataset.

Implications and Future Directions

The findings suggest that addressing frequency distortions is a crucial advancement in post-processing debiasing methods. Double-Hard Debias offers a relatively simple yet effective solution that can be easily integrated into existing pipelines. It opens pathways for more generalizable applications of debiasing across other dimensions beyond gender. The paper encourages further exploration into identifying and mitigating other corpus-induced biases in embeddings.

Conclusion

"Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation" contributes significantly to the ongoing efforts to diminish gender discrimination within NLP systems. By spotlighting the influence of word frequency and introducing a method to counteract it, the paper sets a benchmark for the development of unbiased, high-quality word embeddings. Future research is likely to focus on extending the principles of Double-Hard Debias to address additional biases, ensuring fair and representative LLM outputs.