Considerations for Multilingual Wikipedia Research
This work helps researchers in natural language processing and machine learning by highlighting data equity issues in multilingual datasets, though it is incremental as it synthesizes existing knowledge rather than introducing new methods.
The paper addresses the need for better understanding of content differences across multilingual Wikipedia editions to improve multilingual and multimodal models, providing background on how local context, community governance, and technology cause these differences and offering recommendations for research practices.
English Wikipedia has long been an important data source for much research and natural language machine learning modeling. The growth of non-English language editions of Wikipedia, greater computational resources, and calls for equity in the performance of language and multimodal models have led to the inclusion of many more language editions of Wikipedia in datasets and models. Building better multilingual and multimodal models requires more than just access to expanded datasets; it also requires a better understanding of what is in the data and how this content was generated. This paper seeks to provide some background to help researchers think about what differences might arise between different language editions of Wikipedia and how that might affect their models. It details three major ways in which content differences between language editions arise (local context, community and governance, and technology) and recommendations for good practices when using multilingual and multimodal data for research and modeling.