Gender Bias in Machine Translation

Abstract Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users society at large. As a relatively new field of inquiry, studies gender bias in MT still lack cohesion. This advocates unified framework to ease future research. To this end, we: i) critically review current conceptualizations light theoretical insights related disciplines, ii) summarize previous analyses aimed assessing MT, iii) discuss the mitigating strategies proposed so far, iv) point toward potential directions work.