Language has a profound impact on our thoughts, perceptions, and conceptions of gender roles. Gender-inclusive language is, therefore, a key tool to promote social inclusion and contribute to achieving gender equality. Consequently, detecting and mitigating gender bias in texts is instrumental in halting its propagation and societal implications. However, there is a lack of gender bias datasets and lexicons for automating the detection of gender bias using supervised and unsupervised machine learning (ML) and natural language processing (NLP) techniques. Therefore, the main contribution of this work is to publicly provide labeled datasets and exhaustive lexicons by collecting, annotating, and augmenting relevant sentences to facilitate the detection of gender bias in English text. Towards this end, we present an updated version of our previously proposed taxonomy by re-formalizing its structure, adding a new bias type, and mapping each bias subtype to an appropriate detection methodology. The released datasets and lexicons span multiple bias subtypes including: Generic He, Generic She, Explicit Marking of Sex, and Gendered Neologisms. We leveraged the use of word embedding models to further augment the collected lexicons.

翻译：语言对我们的思想、认知及性别角色观念具有深远影响。因此，性别包容性语言是促进社会包容、助力实现性别平等的关键工具。相应地，检测并减轻文本中的性别偏见对于阻止其传播及社会影响至关重要。然而，目前缺乏用于通过监督式与非监督式机器学习及自然语言处理技术自动化检测性别偏见的数据集与词汇表。因此，本研究的主要贡献在于通过收集、标注并扩充相关句子，公开提供标注数据集与完备词汇表，以辅助英语文本中性别偏见的检测。为此，我们重新形式化了先前提出的分类体系结构，新增一种偏见类型，并将每种偏见子类型映射至相应的检测方法，从而更新了该分类体系。所发布的数据集与词汇表涵盖多种偏见子类型，包括：通用他、通用她、性别显性标记及性别新词。我们利用词嵌入模型进一步扩充了所收集的词汇表。