mlsanityData to 7 Oct 2026

Low-resourced Languages and Online Knowledge Repositories: A Need-Finding Study

arXiv 2405.16669 ↗First posted 26 May 2024cs.HC cs.CLcited by 5

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Abstract (arXiv)

Online Knowledge Repositories (OKRs) like Wikipedia offer communities a way to share and preserve information about themselves and their ways of living. However, for communities with low-resourced languages -- including most African communities -- the quality and volume of content available are often inadequate. One reason for this lack of adequate content could be that many OKRs embody Western ways of knowledge preservation and sharing, requiring many low-resourced language communities to adapt to new interactions. To understand the challenges faced by low-resourced language contributors on the popular OKR Wikipedia, we conducted (1) a thematic analysis of Wikipedia forum discussions and (2) a contextual inquiry study with 14 novice contributors. We focused on three Ethiopian languages: Afan Oromo, Amharic, and Tigrinya. Our analysis revealed several recurring themes; for example, contributors struggle to find resources to corroborate their articles in low-resourced languages, and language technology support, like translation systems and spellcheck, result in several errors that waste contributors' time. We hope our study will support designers in making online knowledge repositories accessible to low-resourced language speakers.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 10 of its 97 references that are in the map. First posted on arXiv 26 May 2024.

Builds on

10 of 97 references.

References · 10

  1. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  2. 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 481
  3. 2020Participatory Research for Low-resourced Machine Translation: A Case Study in African Languagescited by 159
  4. 2021AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processingcited by 131
  5. 2020Mitigating Gender Bias in Machine Translation with Target Gender Annotationscited by 42
  6. 2023WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?cited by 28
  7. 2021Narratives and Counternarratives on Data Sharing in Africacited by 14
  8. 2022When Is TTS Augmentation Through a Pivot Language Useful?cited by 7
  9. 2021Lesan -- Machine Translation for Low Resource Languagescited by 5
  10. 2016Growing Wikipedia Across Languages via Recommendationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.