# Not always about you: Prioritizing community needs when developing endangered language technology

arXiv: https://arxiv.org/abs/2204.05541 · HTML: https://mlsanity.com/paper/2204.05541/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://mlsanity.com/paper/2010.04791/) (2020)
- [Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolox'ochitl Mixtec](https://mlsanity.com/paper/2101.10877/) (2021)
- [Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages](https://mlsanity.com/paper/1804.06024/) (2018)
- [Tackling the Low-resource Challenge for Canonical Segmentation](https://mlsanity.com/paper/2010.02804/) (2020)
- [Bootstrapping Techniques for Polysynthetic Morphological Analysis](https://mlsanity.com/paper/2005.00956/) (2020)

## Built on by (most cited first, 21 of 21)

- [Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers](https://mlsanity.com/paper/2305.19474/) (2023)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects](https://mlsanity.com/paper/2402.11968/) (2024)
- [Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem](https://mlsanity.com/paper/2406.15625/) (2024)
- [Can we teach language models to gloss endangered languages?](https://mlsanity.com/paper/2406.18895/) (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)
- [Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://mlsanity.com/paper/2306.06804/) (2023)
- [LR-Sum: Summarization for Less-Resourced Languages](https://mlsanity.com/paper/2212.09674/) (2022)
- [Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service](https://mlsanity.com/paper/2411.12262/) (2024)
- [A Tulu Resource for Machine Translation](https://mlsanity.com/paper/2403.19142/) (2024)
- [Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participation](https://mlsanity.com/paper/2503.03186/) (2025)
- [Morphological Inflection: A Reality Check](https://mlsanity.com/paper/2305.15637/) (2023)
- [Language Varieties of Italy: Technology Challenges and Opportunities](https://mlsanity.com/paper/2209.09757/) (2022)
- [Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processing](https://mlsanity.com/paper/2404.14740/) (2024)
- [Shaping the Future of Endangered and Low-Resource Languages -- Our Role in the Age of LLMs: A Keynote at ECIR 2024](https://mlsanity.com/paper/2409.13702/) (2024)
- [Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models](https://mlsanity.com/paper/2410.19812/) (2024)
- [JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT](https://mlsanity.com/paper/2210.02545/) (2022)
- [Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language](https://mlsanity.com/paper/2410.13313/) (2024)
- [Meenz bleibt Meenz, but Large Language Models Do Not Speak Its Dialect](https://mlsanity.com/paper/2602.16852/) (2026)
- [Vavanagi: a Community-run Platform for Documentation of the Hula Language in Papua New Guinea](https://mlsanity.com/paper/2603.14210/) (2026)
- [textitVersteasch du mi? Computational and Socio-Linguistic Perspectives on GenAI, LLMs, and Non-Standard Language](https://mlsanity.com/paper/2603.28213/) (2026)

## Abstract (arXiv)

Languages are classified as low-resource when they lack the quantity of data necessary for training statistical and machine learning tools and models. Causes of resource scarcity vary but can include poor access to technology for developing these resources, a relatively small population of speakers, or a lack of urgency for collecting such resources in bilingual populations where the second language is high-resource. As a result, the languages described as low-resource in the literature are as different as Finnish on the one hand, with millions of speakers using it in every imaginable domain, and Seneca, with only a small-handful of fluent speakers using the language primarily in a restricted domain. While issues stemming from the lack of resources necessary to train models unite this disparate group of languages, many other issues cut across the divide between widely-spoken low resource languages and endangered languages. In this position paper, we discuss the unique technological, cultural, practical, and ethical challenges that researchers and indigenous speech community members face when working together to develop language technology to support endangered language documentation and revitalization. We report the perspectives of language teachers, Master Speakers and elders from indigenous communities, as well as the point of view of academics. We describe an ongoing fruitful collaboration and make recommendations for future partnerships between academic researchers and language community stakeholders.