Towards Debiasing Sentence Representations (2007.08100v1)

Abstract: As natural language processing methods are increasingly deployed in real-world scenarios such as healthcare, legal systems, and social science, it becomes necessary to recognize the role they potentially play in shaping social biases and stereotypes. Previous work has revealed the presence of social biases in widely used word embeddings involving gender, race, religion, and other social constructs. While some methods were proposed to debias these word-level embeddings, there is a need to perform debiasing at the sentence-level given the recent shift towards new contextualized sentence representations such as ELMo and BERT. In this paper, we investigate the presence of social biases in sentence-level representations and propose a new method, Sent-Debias, to reduce these biases. We show that Sent-Debias is effective in removing biases, and at the same time, preserves performance on sentence-level downstream tasks such as sentiment analysis, linguistic acceptability, and natural language understanding. We hope that our work will inspire future research on characterizing and removing social biases from widely adopted sentence representations for fairer NLP.

Towards Debiasing Sentence Representations

The paper, entitled "Towards Debiasing Sentence Representations," addresses a critical concern in the application of NLP systems: the propagation of social biases and stereotypes, particularly as NLP methodologies are increasingly applied in sensitive real-world contexts such as healthcare, legal systems, and social sciences. As the field of NLP evolves, significant attention has been placed on understanding and mitigating biases found within word embeddings. However, with the advent of sophisticated contextualized sentence representations like ELMo and BERT, the need for debiasing extends beyond individual words to encompass entire sentences.

Key Contributions

The authors present a novel approach, Sent-Debias, aimed at reducing biases encoded within sentence-level representations. This method follows a structured process consisting of the following steps:

1. Defining Bias Attributes: Identification and specification of words that demonstrate bias, involving both binary attributes (e.g., gender) and multiclass attributes (e.g., religion).
2. Contextualizing Words into Sentences: Utilizing diverse sentence templates derived from large corpora to naturally contextualize bias-attributes, thereby forming a dataset conducive to analyzing sentence biases.
3. Estimating Bias Subspace: Employing principal component analysis (PCA) to discern the primary components representing bias within sentence representations.
4. Debiasing Process: Implementing a variant of the Hard-Debias method to remove identified bias components, ensuring sentences are orthogonal to the bias subspace.

Experimental Evaluation

The main empirical findings emphasize the efficacy of Sent-Debias in diminishing bias while preserving task performance on downstream applications such as sentiment analysis and grammaticality judgment. Using standard metrics like WEAT and its extension for sentences, SEAT, the paper benchmarks the effectiveness of debiasing across several widely-used models, including BERT and ELMo.

The results demonstrate a noticeable reduction in bias, quantified by effect sizes moving closer to zero post-debiasing. The paper also engages with comparisons to baseline methods, showing that Sent-Debias outperforms traditional word-level debiasing techniques when evaluated on sentence tasks.

Implications and Future Directions

The implications of debiasing at the sentence level are substantial for the broader field of AI ethics and fairness in machine learning. Although the Sent-Debias method provides an effective post-hoc approach to tackle biases, the paper acknowledges several limitations. These include the challenge in verifying the complete absence of bias, the complex identification of neutral sentences, and the practicality of recalibrating debiasing post-fine-tuning.

Moving forward, developments in AI will need to address these limitations, possibly through frameworks that understand bias on an application-specific basis or methods that seamlessly integrate bias correction into the training pipelines of NLP models. Enhanced metrics and debiasing strategies could lead to more robust and ethically aligned AI systems, essential as these technologies permeate socially sensitive domains.

In conclusion, "Towards Debiasing Sentence Representations" sets a precedent for future research in sentence-level bias mitigation, advocating for fairer and more equitable NLP systems. The work aligns with ongoing efforts within computational ethics to ensure that AI applications do not inadvertently perpetuate societal disparities.