FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders (2103.06413v1)

Abstract: Pretrained text encoders, such as BERT, have been applied increasingly in various NLP tasks, and have recently demonstrated significant performance gains. However, recent studies have demonstrated the existence of social bias in these pretrained NLP models. Although prior works have made progress on word-level debiasing, improved sentence-level fairness of pretrained encoders still lacks exploration. In this paper, we proposed the first neural debiasing method for a pretrained sentence encoder, which transforms the pretrained encoder outputs into debiased representations via a fair filter (FairFil) network. To learn the FairFil, we introduce a contrastive learning framework that not only minimizes the correlation between filtered embeddings and bias words but also preserves rich semantic information of the original sentences. On real-world datasets, our FairFil effectively reduces the bias degree of pretrained text encoders, while continuously showing desirable performance on downstream tasks. Moreover, our post-hoc method does not require any retraining of the text encoders, further enlarging FairFil's application space.

A Critical Analysis of "FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders"

The paper "FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders" introduces a novel approach for mitigating social biases in pretrained text encoders, particularly aiming to enhance fairness at the sentence level. This research presents FairFil, a debiasing method that operates via a contrastive learning framework, significantly contributing to the field of fairness in NLP.

Social biases in pretrained models like BERT are of increasing concern. These biases often stem from the data on which the models are trained and manifest in tasks such as sentence representation. Existing literature has largely focused on word-level debiasing, which fails to address the pervasive issue of sentence-level bias. Additionally, methods like Sent-Debias, which assume linear bias removal, have manifested limitations in generalizability. FairFil overcomes these limitations by applying a non-linear debiasing approach, eliminating the necessity to retrain the entire model, thus offering a pragmatic solution in terms of computational efficiency and applicability.

The paper outlines the FairFil's operation, which debiases sentence encoders by transforming output embeddings through a fair filter network. This transformation is learned using a contrastive learning setup where each sentence, alongside its augmented pair with different potential bias directions, is employed. The augmentation process is carefully curated to ensure semantic similarity while varying bias directions, and mutual information is maximized between the embeddings of these pairs. Notably, a debiasing regularizer is incorporated to minimize mutual information between the debiased sentence embeddings and sensitive word embeddings, further enhancing the debiasing effectiveness.

Empirical validation is provided through comparisons with existing methods, primarily using the Sentence Encoder Association Test (SEAT) to measure bias degrees and the performance of debiased embeddings on NLP downstream tasks. FairFil demonstrates superior performance in bias reduction while maintaining or improving classification accuracy. Remarkably, FairFil achieves robust results across multiple datasets and tasks, indicative of its wider applicability.

Implications of this work extend beyond mere bias reduction; the method allows preprocessing of encoder outputs to be more socially fair, which can subsequently improve trust and applicability of NLP systems in sensitive domains. Importantly, this debiasing method does not require access to the original model training data or necessitate retraining, broadening its utility.

Future research could explore expanding FairFil to address other nuanced biases and test its efficacy across various languages and contexts. Moreover, integration of FairFil into real-time applications could be a compelling next step to actively teach NLP models fairer interactions.

Overall, this paper makes a substantial contribution to the field by addressing a critical limitation of existing debiasing methods. It does so through a careful, technically sound methodology that innovatively leverages contrastive learning to achieve more fair sentence-level representations. The potential for FairFil to be refined and applied in diverse contexts leaves room for continuous improvement and adoption in future NLP applications.