  This work presents an empirical approach to quantifying the loss of lexical
richness in Machine Translation (MT) systems compared to Human Translation
(HT). Our experiments show how current MT systems indeed fail to render the
lexical diversity of human generated or translated text. The inability of MT
systems to generate diverse outputs and its tendency to exacerbate already
frequent patterns while ignoring less frequent ones, might be the underlying
cause for, among others, the currently heavily debated issues related to gender
biased output. Can we indeed, aside from biased data, talk about an algorithm
that exacerbates seen biases?
