  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.
