mlsanityData to 14 Oct 2026

What Do Indonesians Really Need from Language Technology? A Nationwide Survey

arXiv 2506.07506 ↗First posted 9 Jun 2025cs.CLcited by 3

Not on the map. Why?

Abstract (arXiv)

There is an emerging effort to develop NLP for Indonesias 700+ local languages, but progress remains costly due to the need for direct engagement with native speakers. However, it is unclear what these language communities truly need from language technology. To address this, we conduct a nationwide survey to assess the actual needs of native speakers in Indonesia. Our findings indicate that addressing language barriers, particularly through machine translation and information retrieval, is the most critical priority. Although there is strong enthusiasm for advancements in language technology, concerns around privacy, bias, and the use of public data for AI training highlight the need for greater transparency and clear communication to support broader AI adoption.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 50 references that are in the map. First posted on arXiv 9 Jun 2025.

Builds on

15 of 50 references.

References · 15

  1. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  2. 2022NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languagescited by 68
  3. 2022One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesiacited by 66
  4. 2020IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLPcited by 58
  5. 2023Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLUcited by 53
  6. 2024SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languagescited by 43
  7. 2022NusaCrowd: Open Source Initiative for Indonesian NLP Resourcescited by 38
  8. 2021Many-to-English Machine Translation Tools, Data, and Pretrained Modelscited by 31
  9. 2022What a Creole Wants, What a Creole Needscited by 27
  10. 2023The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Researchcited by 27
  11. 2023Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakerscited by 27
  12. 2024What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialectscited by 21
3 more
  1. 2024"It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologiescited by 12
  2. 2020Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesiscited by 8
  3. 2023NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languagescited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Grounding AI-in-Education Development in Teachers' Voices: Findings from a National Survey in Indonesiacited by 0
  2. 2026Romanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variationcited by 0
  3. 2026Towards the Roof of the World: NLP Progress in Tibetan Languagescited by 0