  Most works on gender bias focus on intrinsic bias -- removing traces of
information about a protected group from the model's internal representation.
However, these works are often disconnected from the impact of such debiasing
on downstream applications, which is the main motivation for debiasing in the
first place. In this work, we systematically test how methods for intrinsic
debiasing affect neural machine translation models, by measuring the extrinsic
bias of such systems under different design choices. We highlight three
challenges and mismatches between the debiasing techniques and their end-goal
usage, including the choice of embeddings to debias, the mismatch between words
and sub-word tokens debiasing, and the effect on different target languages. We
find that these considerations have a significant impact on downstream
performance and the success of debiasing.
