  Natural language generation models reproduce and often amplify the biases
present in their training data. Previous research explored using
sequence-to-sequence rewriting models to transform biased model outputs (or
original texts) into more gender-fair language by creating pseudo training data
through linguistic rules. However, this approach is not practical for languages
with more complex morphology than English. We hypothesise that creating
training data in the reverse direction, i.e. starting from gender-fair text, is
easier for morphologically complex languages and show that it matches the
performance of state-of-the-art rewriting models for English. To eliminate the
rule-based nature of data creation, we instead propose using machine
translation models to create gender-biased text from real gender-fair text via
round-trip translation. Our approach allows us to train a rewriting model for
German without the need for elaborate handcrafted rules. The outputs of this
model increased gender-fairness as shown in a human evaluation study.
