论文标题
通过后正规化缓解分布的性别偏差扩增
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization
论文作者
论文摘要
先进的机器学习技术提高了自然语言处理的性能。然而，最近的研究，例如Zhao等。 （2017年）表明，这些技术无意中捕获了隐藏在语料库中的社会偏见，并进一步扩大了它。但是，他们的分析仅在模型的最高预测上进行。在本文中，我们从分布的角度研究了性别偏差放大问题，并证明了偏见是在预测的概率分布上放大了偏差。我们进一步提出了一种基于后正则化的缓解方法。在绩效损失的情况下，我们的方法几乎可以消除分布中的偏置扩增。我们的研究阐明了了解偏置扩增的启示。
Advanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., Zhao et al. (2017) show that these techniques inadvertently capture the societal bias hidden in the corpus and further amplify it. However, their analysis is conducted only on models' top predictions. In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization. With little performance loss, our method can almost remove the bias amplification in the distribution. Our study sheds the light on understanding the bias amplification.
