Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models

Abstract

Over the last few years, Contextualized Pre-trained Neural Language Models, such as BERT, GPT, have shown significant gains in various NLP tasks. To enhance the robustness of existing pre-trained models, one way is adversarial examples generation and evaluation for conducting data augmentation or adversarial learning. In the meanwhile, gender bias embedded in the models seems to be a serious problem in practical applications. Many researches have covered the gender bias produced by word-level information(e.g. gender-stereotypical occupations), while few researchers have investigated the sentence-level cases and implicit cases. In this paper, we proposed a method to automatically generate implicit gender bias samples at sentence-level and a metric to measure gender bias. Samples generated by our method will be evaluated in terms of accuracy. The metric will be used to guide the generation of examples from Pre-trained models. Therefore, those examples could be used to impose attacks on Pre-trained Models. Finally, we discussed the evaluation efficacy of our generated examples on reducing gender bias for future research.

References in corpus (5)

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
• Adversarial Training for Large Neural Language Models
• Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation
• Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer