Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to unbalanced grammatical gender frequencies of inflected words. This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context. As a step towards more inclusive NLP, this paper proposes an automatic and generalisable rewriting approach for short conversational sentences. The rewriting method can be applied to sentences that, without extra-sentential context, have multiple equivalent alternatives in terms of gender. The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data. The proposed approach is based on a neural machine translation (NMT) system trained to 'translate' from one gender alternative to another. Both the automatic and manual analysis of the approach show promising results for automatic generation of gender alternatives for conversational sentences in Spanish.

翻译：性别偏见在基于语言的NLP应用中是一种常见现象,特别是在具有性别色彩的语言中尤为明显。Bias可以通过某些形容词和动词名的组合形式出现,与引用的自然性别有关,但同时也由于偏向的语法性别频率不平衡。这种偏见在生成谈话语句时更加明显,因为在句子中没有具体说明性别,因为大多数当前的NLP应用软件仍然在判决级别上起作用。作为向更具包容性的NLP迈出的一步,本文提议了一种自动和通用的短话重写方法。改写方法可以适用于没有超常背景、在性别方面具有多种等同替代语言的句子。该方法既可用于创造性别平衡的产出,又可用于创建性别平衡的培训数据。拟议方法基于神经机翻译系统,经过培训,从一种性别替代语言“翻译”到另一种语言。对方法的自动和人工分析都显示在西班牙语中自动生成性别替代语言谈话语句的预期结果。