# Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service

arXiv: https://arxiv.org/abs/2411.12262 · HTML: https://mlsanity.com/paper/2411.12262/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://mlsanity.com/paper/2309.04662/) (2023)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers](https://mlsanity.com/paper/2305.19474/) (2023)
- [Not always about you: Prioritizing community needs when developing endangered language technology](https://mlsanity.com/paper/2204.05541/) (2022)
- [What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects](https://mlsanity.com/paper/2402.11968/) (2024)
- [Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity](https://mlsanity.com/paper/2402.02633/) (2024)
- [The Zeno's Paradox of `Low-Resource' Languages](https://mlsanity.com/paper/2410.20817/) (2024)
- [Exploring Large Language Models for Relevance Judgments in Tetun](https://mlsanity.com/paper/2406.07299/) (2024)

## Built on by (most cited first, 5 of 5)

- [The Impact of AI-Generated Text on the Internet](https://mlsanity.com/paper/2604.26965/) (2026)
- [Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent](https://mlsanity.com/paper/2606.03259/) (2026)
- [SEA-SafeguardBench: Evaluating AI Safety in SEA Languages and Cultures](https://mlsanity.com/paper/2512.05501/) (2025)
- [Vavanagi: a Community-run Platform for Documentation of the Hula Language in Papua New Guinea](https://mlsanity.com/paper/2603.14210/) (2026)
- [MUDIDI: A Two-Stage Framework for Multilingual Dictionary Digitization with Language Models](https://mlsanity.com/paper/2606.09435/) (2026)

## Abstract (arXiv)

Low-resource machine translation (MT) presents a diversity of community needs and application challenges that remain poorly understood. To complement surveys and focus groups, which tend to rely on small samples of respondents, we propose an observational study on actual usage patterns of tetun.org, a specialized MT service for the Tetun language, which is the lingua franca in Timor-Leste. Our analysis of 100,000 translation requests reveals patterns that challenge assumptions based on existing corpora. We find that users, many of them students on mobile devices, typically translate text from a high-resource language into Tetun across diverse domains including science, healthcare, and daily life. This contrasts sharply with available Tetun corpora, which are dominated by news articles covering government and social issues. Our results suggest that MT systems for institutionalized minority languages like Tetun should prioritize accuracy on domains relevant to educational contexts, in the high-resource to low-resource direction. More broadly, this study demonstrates how observational analysis can inform low-resource language technology development, by grounding research in practical community needs.