Dual Debiasing: Remove Stereotypes and Keep Factual Gender for Fair Language Modeling and Translation
This addresses fairness issues in language technology for users by mitigating biases without compromising model capabilities, though it appears incremental as it builds on existing debiasing methods.
The paper tackles the problem of gender stereotypes in language models and translation by introducing the Dual Debiasing Algorithm through Model Adaptation (2DAMA), which reduces stereotypical bias while preserving factual gender information, showing effectiveness in English and translation tasks.
Mitigation of biases, such as language models' reliance on gender stereotypes, is a crucial endeavor required for the creation of reliable and useful language technology. The crucial aspect of debiasing is to ensure that the models preserve their versatile capabilities, including their ability to solve language tasks and equitably represent various genders. To address this issue, we introduce a streamlined Dual Dabiasing Algorithm through Model Adaptation (2DAMA). Novel Dual Debiasing enables robust reduction of stereotypical bias while preserving desired factual gender information encoded by language models. We show that 2DAMA effectively reduces gender bias in English and is one of the first approaches facilitating the mitigation of stereotypical tendencies in translation. The proposed method's key advantage is the preservation of factual gender cues, which are useful in a wide range of natural language processing tasks.