We examine whether neural natural language processing (NLP) systems reflect
historical biases in training data. We define a general benchmark to quantify
gender bias in a variety of neural NLP tasks. Our empirical evaluation with
state-of-the-art neural coreference resolution and textbook RNN-based language
models trained on benchmark datasets finds significant gender bias in how
models view occupations. We then mitigate bias with CDA: a generic methodology
for corpus augmentation via causal interventions that breaks associations
between gendered and gender-neutral words. We empirically show that CDA
effectively decreases gender bias while preserving accuracy. We also explore
the space of mitigation strategies with CDA, a prior approach to word embedding
debiasing (WED), and their compositions. We show that CDA outperforms WED,
drastically so when word embeddings are trained. For pre-trained embeddings,
the two methods can be effectively composed. We also find that as training
proceeds on the original data set with gradient descent the gender bias grows
as the loss reduces, indicating that the optimization encourages bias; CDA
mitigates this behavior.

本文研究了神经自然语言处理系统是否反映了训练数据中的历史偏见。通过定义量化神经自然语言处理任务中的性别偏见的基准测试，实证研究发现目前最先进的神经语言模型和基于基准数据集的循环神经网络在职业看待上存在显著的性别偏见。作者提出了一种基于因果干预的语料库增强通用方法，CDA，以减轻偏见而保留矫正前的准确性。作者发现 CDA 优于词嵌入去偏的先前方法，尤其是在词嵌入训练时；对于预先训练的嵌入，两种方法可以被有效地组合。作者还发现原始数据集在优化时会鼓励性别偏见增加；而 CDA 可以缓解这种行为。