Park et al. (2018) discuss different ways of mitigating gender bias, in the context of abusive language detection, ranging from debiasing a model by using the hard debiased word embeddings produced by<cite> Bolukbasi et al. (2016b)</cite> , to manipulating the data prior to training a model by swapping masculine and feminine mentions, and employing transfer learning from a model learned from less biased text.