Issue #119 – Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in MT

25 Feb21

This week we have a guest post from Eva Vanmassenhove, Assistant Professor at Tilburg University, Dimitar Shterionov, Assistant Professor at Tilburg University, and Matt Gwilliam, from the University of Maryland.

In Translation Studies, it is common to refer to a term called “translationese” that encapsulates a set of linguistic features commonly present in human translations as opposed to originally written texts. Researchers in the Machine Translation field have explored how this could be transposed to post-editing tasks and hence talk about “post-editese” (if you are curious, this paper by Joke Daems et al. may be a good starting point). Eva and her colleagues go one step further