# How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language

arXiv: https://arxiv.org/abs/2204.11909 · HTML: https://mlsanity.com/paper/2204.11909/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 20 of 20)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [Byte Pair Encoding is Suboptimal for Language Model Pretraining](https://mlsanity.com/paper/2004.03720/) (2020)
- [CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://mlsanity.com/paper/1911.04944/) (2019)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings](https://mlsanity.com/paper/1811.01136/) (2018)
- [Compound Probabilistic Context-Free Grammars for Grammar Induction](https://mlsanity.com/paper/1906.10225/) (2019)
- [LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition](https://mlsanity.com/paper/2008.03687/) (2020)
- [Effective Parallel Corpus Mining using Bilingual Sentence Embeddings](https://mlsanity.com/paper/1807.11906/) (2018)
- [OCR Post Correction for Endangered Language Texts](https://mlsanity.com/paper/2011.05402/) (2020)
- [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://mlsanity.com/paper/2010.04791/) (2020)
- [How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?](https://mlsanity.com/paper/2109.01100/) (2021)
- [ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback](https://mlsanity.com/paper/2107.14800/) (2021)

## Built on by (most cited first, 19 of 19)

- [Lost in Translation: Large Language Models in Non-English Content Analysis](https://mlsanity.com/paper/2306.07377/) (2023)
- [Quantifying the Dialect Gap and its Correlates Across Languages](https://mlsanity.com/paper/2310.15135/) (2023)
- [Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers](https://mlsanity.com/paper/2305.19474/) (2023)
- [Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research](https://mlsanity.com/paper/2412.04497/) (2024)
- [Can we teach language models to gloss endangered languages?](https://mlsanity.com/paper/2406.18895/) (2024)
- [Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation](https://mlsanity.com/paper/2411.11295/) (2024)
- [Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://mlsanity.com/paper/2306.06804/) (2023)
- [Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Models](https://mlsanity.com/paper/2210.15184/) (2022)
- [Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions](https://mlsanity.com/paper/2302.04975/) (2023)
- [Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languages](https://mlsanity.com/paper/2501.15773/) (2025)
- [Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participation](https://mlsanity.com/paper/2503.03186/) (2025)
- [DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms](https://mlsanity.com/paper/2503.03644/) (2025)
- [NushuRescue: Revitalization of the Endangered Nushu Language with AI](https://mlsanity.com/paper/2412.00218/) (2024)
- [Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging?](https://mlsanity.com/paper/2206.14969/) (2022)
- [JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT](https://mlsanity.com/paper/2210.02545/) (2022)
- [Shifting from endangerment to rebirth in the Artificial Intelligence Age: An Ensemble Machine Learning Approach for Hawrami Text Classification](https://mlsanity.com/paper/2409.16884/) (2024)
- [LakotaBERT: A Transformer-based Model for Low Resource Lakota Language](https://mlsanity.com/paper/2503.18212/) (2025)
- [Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages](https://mlsanity.com/paper/2511.14598/) (2025)
- [Diacritic Restoration for Low-Resource Indigenous Languages: Case Study with Bribri and Cook Islands M=aori](https://mlsanity.com/paper/2512.19630/) (2025)

## Abstract (arXiv)

More than 43% of the languages spoken in the world are endangered, and language loss currently occurs at an accelerated rate because of globalization and neocolonialism. Saving and revitalizing endangered languages has become very important for maintaining the cultural diversity on our planet. In this work, we focus on discussing how NLP can help revitalize endangered languages. We first suggest three principles that may help NLP practitioners to foster mutual understanding and collaboration with language communities, and we discuss three ways in which NLP can potentially assist in language education. We then take Cherokee, a severely-endangered Native American language, as a case study. After reviewing the language's history, linguistic features, and existing resources, we (in collaboration with Cherokee community members) arrive at a few meaningful ways NLP practitioners can collaborate with community partners. We suggest two approaches to enrich the Cherokee language's resources with machine-in-the-loop processing, and discuss several NLP tools that people from the Cherokee community have shown interest in. We hope that our work serves not only to inform the NLP community about Cherokee, but also to provide inspiration for future work on endangered languages in general. Our code and data will be open-sourced at https://github.com/ZhangShiyue/RevitalizeCherokee