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.

翻译：自然语言生成模型会复现并通常放大其训练数据中存在的偏见。先前研究探索使用序列到序列改写模型，通过基于语言规则创建伪训练数据，将偏见的模型输出（或原始文本）转化为更性别公平的语言。然而，这种方法对于形态学比英语更复杂的语言并不实用。我们假设，反向创建训练数据（即从性别公平文本出发）在形态复杂的语言中更易实现，并证明其性能与英语最先进的改写模型相当。为消除数据创建中的规则依赖性，我们转而提出利用机器翻译模型，通过往返翻译从真实性别公平文本生成性别偏见文本。该方法使我们能够在无需复杂手工规则的情况下训练德语的改写模型。人类评估研究表明，该模型的输出显著提升了性别公平性。