Lack of diverse perspectives causes neutrality bias in Wikipedia content
leading to millions of worldwide readers getting exposed by potentially
inaccurate information. Hence, neutrality bias detection and mitigation is a
critical problem. Although previous studies have proposed effective solutions
for English, no work exists for Indian languages. First, we contribute two
large datasets, mWikiBias and mWNC, covering 8 languages, for the bias
detection and mitigation tasks respectively. Next, we investigate the
effectiveness of popular multilingual Transformer-based models for the two
tasks by modeling detection as a binary classification problem and mitigation
as a style transfer problem. We make the code and data publicly available.

缺乏多元观点导致维基百科内容中的中立偏见，从而使数百万全球读者接触到潜在的不准确信息。因此，中立偏见的检测和缓解是一个关键问题。我们为此提供了两个大型数据集 mWikiBias 和 mWNC，分别涵盖 8 种语言，用于中立偏见的检测和缓解任务。接下来，我们通过将检测建模为二元分类问题和将缓解建模为样式转换问题，研究了流行的多语言基于 Transformer 的模型在这两个任务中的有效性。我们将代码和数据公开提供。