论文标题
轻松适应以减轻多语言文本分类中的性别偏见
Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification
论文作者
论文摘要
现有的减轻人口偏见的方法对单语数据进行了评估，但是尚未检查多语言数据。在这项工作中，我们将性别视为领域（例如，男性与女性），并提出了标准的域适应模型，以减少性别偏见并提高多语言环境下的文本分类器的性能。我们在两个文本分类任务，仇恨言语检测和评级预测上评估我们的方法，并通过三个公平意识的基线证明了我们方法的有效性。
Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers under multilingual settings. We evaluate our approach on two text classification tasks, hate speech detection and rating prediction, and demonstrate the effectiveness of our approach with three fair-aware baselines.
