  When translating "The secretary asked for details." to a language with
grammatical gender, it might be necessary to determine the gender of the
subject "secretary". If the sentence does not contain the necessary
information, it is not always possible to disambiguate. In such cases, machine
translation systems select the most common translation option, which often
corresponds to the stereotypical translations, thus potentially exacerbating
prejudice and marginalisation of certain groups and people. We argue that the
information necessary for an adequate translation can not always be deduced
from the sentence being translated or even might depend on external knowledge.
Therefore, in this work, we propose to decouple the task of acquiring the
necessary information from the task of learning to translate correctly when
such information is available. To that end, we present a method for training
machine translation systems to use word-level annotations containing
information about subject's gender. To prepare training data, we annotate
regular source language words with grammatical gender information of the
corresponding target language words. Using such data to train machine
translation systems reduces their reliance on gender stereotypes when
information about the subject's gender is available. Our experiments on five
language pairs show that this allows improving accuracy on the WinoMT test set
by up to 25.8 percentage points.
