  Adversarial training is a common approach for bias mitigation in natural
language processing. Although most work on debiasing is motivated by equal
opportunity, it is not explicitly captured in standard adversarial training. In
this paper, we propose an augmented discriminator for adversarial training,
which takes the target class as input to create richer features and more
explicitly model equal opportunity. Experimental results over two datasets show
that our method substantially improves over standard adversarial debiasing
methods, in terms of the performance--fairness trade-off.
