mlsanityData to 14 Oct 2026

NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages

arXiv 2309.10661 ↗First posted 19 Sep 2023cs.CL cs.AIcited by 5

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Abstract (arXiv)

Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the datasetname benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages. We release the NusaWrites dataset at https://github.com/IndoNLP/nusa-writes.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 33 of its 85 references that are in the map. First posted on arXiv 19 Sep 2023.

Builds on

33 of 85 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025The Multilingual Divide and Its Impact on Global AI Safetycited by 10
  2. 2025Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asiacited by 9
  3. 2025LoraxBench: A Multitask, Multilingual Benchmark Suite for 20 Indonesian Languagescited by 6
  4. 2025What Do Indonesians Really Need from Language Technology? A Nationwide Surveycited by 3
  5. 2026Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languagescited by 0