As a result of this bias, the embeddings may cause undesired consequences in the resulting models (Zhao et al., 2018a;Font and Costa-jussà, 2019).
extensive effort has been put toward analyzing and mitigating gender bias in word embeddings (Bolukbasi et al., 2016;Zhao et al., 2018b;Dev and Phillips, 2019;Ethayarajh et al., 2019).