# Considerations for Multilingual Wikipedia Research

arXiv: https://arxiv.org/abs/2204.02483 · HTML: https://mlsanity.com/paper/2204.02483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 69 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Corpora Generation for Grammatical Error Correction](https://mlsanity.com/paper/1904.05780/) (2019)
- [WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community](https://mlsanity.com/paper/1810.13181/) (2018)
- [Why the World Reads Wikipedia: Beyond English Speakers](https://mlsanity.com/paper/1812.00474/) (2018)
- [Controlled Analyses of Social Biases in Wikipedia Bios](https://mlsanity.com/paper/2101.00078/) (2020)
- [The Wisdom of Polarized Crowds](https://mlsanity.com/paper/1712.06414/) (2017)
- [Understanding Editing Behaviors in Multilingual Wikipedia](https://mlsanity.com/paper/1508.07266/) (2015)
- [Not at Home on the Range: Peer Production and the Urban/Rural Divide](https://mlsanity.com/paper/1908.10954/) (2019)
- [Language-agnostic Topic Classification for Wikipedia](https://mlsanity.com/paper/2103.00068/) (2021)
- [Wikipedia Reader Navigation: When Synthetic Data Is Enough](https://mlsanity.com/paper/2201.00812/) (2022)
- [Crosslingual Topic Modeling with WikiPDA](https://mlsanity.com/paper/2009.11207/) (2020)
- [Wikipedia Cultural Diversity Dataset: A Complete Cartography for 300 Language Editions](https://mlsanity.com/paper/1901.07999/) (2019)
- [Global gender differences in Wikipedia readership](https://mlsanity.com/paper/2007.10403/) (2020)
- [WikiHist.html: English Wikipedia's Full Revision History in HTML Format](https://mlsanity.com/paper/2001.10256/) (2020)
- [Wikipedia Text Reuse: Within and Without](https://mlsanity.com/paper/1812.09221/) (2018)
- [Analyzing Wikidata Transclusion on English Wikipedia](https://mlsanity.com/paper/2011.00997/) (2020)

## Built on by (most cited first, 1 of 1)

- [Where's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities](https://mlsanity.com/paper/2610.00606/) (2026)

## Abstract (arXiv)

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.