Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in multilingual NLP. In the context of low-resource languages, however, these quality assumptions are increasingly being scrutinised. This paper critically examines the data quality of Wikipedia in a non-English setting by subjecting it to various quality filtering techniques, revealing widespread issues such as a high percentage of one-line articles and duplicate articles. We evaluate the downstream impact of quality filtering on Wikipedia and find that data quality pruning is an effective means for resource-efficient training without hurting performance, especially for low-resource languages. Moreover, we advocate for a shift in perspective from seeking a general definition of data quality towards a more language- and task-specific one. Ultimately, we aim for this study to serve as a guide to using Wikipedia for pretraining in a multilingual setting.

本研究探讨了维基百科在低资源语言环境中的数据质量问题，发现存在大量一行和重复文章等质量问题。通过实施质量过滤技术，我们证明数据质量修剪可以在不损害性能的情况下，提高资源效率，特别适用于低资源语言。我们提倡从语言和任务特定的角度重新定义数据质量，以指导多语言预训练中的维基百科使用。

维基百科的质量如何？