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

arXiv: https://arxiv.org/abs/2506.07506 · HTML: https://mlsanity.com/paper/2506.07506/

## 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 (most cited first, 15 of 15)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages](https://mlsanity.com/paper/2205.15960/) (2022)
- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP](https://mlsanity.com/paper/2011.00677/) (2020)
- [Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU](https://mlsanity.com/paper/2310.04928/) (2023)
- [SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages](https://mlsanity.com/paper/2406.10118/) (2024)
- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [Many-to-English Machine Translation Tools, Data, and Pretrained Models](https://mlsanity.com/paper/2104.00290/) (2021)
- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research](https://mlsanity.com/paper/2305.02797/) (2023)
- [Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers](https://mlsanity.com/paper/2305.19474/) (2023)
- [What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects](https://mlsanity.com/paper/2402.11968/) (2024)
- ["It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologies](https://mlsanity.com/paper/2402.02639/) (2024)
- [Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesis](https://mlsanity.com/paper/2011.02128/) (2020)
- [NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages](https://mlsanity.com/paper/2309.10661/) (2023)

## Built on by (most cited first, 3 of 3)

- [Grounding AI-in-Education Development in Teachers' Voices: Findings from a National Survey in Indonesia](https://mlsanity.com/paper/2604.01630/) (2026)
- [Romanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variation](https://mlsanity.com/paper/2608.02555/) (2026)
- [Towards the Roof of the World: NLP Progress in Tibetan Languages](https://mlsanity.com/paper/2610.13398/) (2026)

## 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.