Model-based Counterfactual Generator for Gender Bias Mitigation

Counterfactual Data Augmentation (CDA) has been one of the preferred techniques for mitigating gender bias in natural language models. CDA techniques have mostly employed word substitution based on dictionaries. Although such dictionary-based CDA techniques have been shown to significantly improve the mitigation of gender bias, in this paper, we highlight some limitations of such dictionary-based counterfactual data augmentation techniques, such as susceptibility to ungrammatical compositions, and lack of generalization outside the set of predefined dictionary words. Model-based solutions can alleviate these problems, yet the lack of qualitative parallel training data hinders development in this direction. Therefore, we propose a combination of data processing techniques and a bi-objective training regime to develop a model-based solution for generating counterfactuals to mitigate gender bias. We implemented our proposed solution and performed an empirical evaluation which shows how our model alleviates the shortcomings of dictionary-based solutions.