mlsanityData to 7 Oct 2026

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

arXiv 2204.11909 ↗First posted 25 Apr 2022cs.CL cs.AIcited by 19

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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

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 20 of its 71 references that are in the map. First posted on arXiv 25 Apr 2022.

Builds on

20 of 71 references.

References · 20

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8 more
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Citing papers per year: 2022: 3; 2023: 5; 2024: 5; 2025: 6.

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  1. 2023Lost in Translation: Large Language Models in Non-English Content Analysiscited by 34
  2. 2023Quantifying the Dialect Gap and its Correlates Across Languagescited by 33
  3. 2023Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakerscited by 26
  4. 2024Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Researchcited by 26
  5. 2024Can we teach language models to gloss endangered languages?cited by 14
  6. 2024Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translationcited by 12
  7. 2023Neural Machine Translation for the Indigenous Languages of the Americas: An Introductioncited by 7
  8. 2022Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Modelscited by 5
  9. 2023Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptionscited by 4
  10. 2025Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languagescited by 3
  11. 2025Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participationcited by 3
  12. 2025DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictogramscited by 3
7 more
  1. 2024NushuRescue: Revitalization of the Endangered Nushu Language with AIcited by 1
  2. 2022Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging?cited by 0
  3. 2022JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMTcited by 0
  4. 2024Shifting from endangerment to rebirth in the Artificial Intelligence Age: An Ensemble Machine Learning Approach for Hawrami Text Classificationcited by 0
  5. 2025LakotaBERT: A Transformer-based Model for Low Resource Lakota Languagecited by 0
  6. 2025Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languagescited by 0
  7. 2025Diacritic Restoration for Low-Resource Indigenous Languages: Case Study with Bribri and Cook Islands M=aoricited by 0