Visibility as Survival: Generalizing NLP for Native Alaskan Language Identification

Challenge:	Indigenous languages are largely invisible in commercial language identification systems, a stark reality exemplified by Google Translate’s LangID tool, which excludes all 150 Indigenous languages of North America.
Approach:	They propose a framework that shows how large language models and specialized classifiers can effectively identify these languages with minimal data.
Outcome:	The proposed framework shows that large language models and specialized classifiers can effectively identify these languages with minimal data.

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Overview

Challenge

Approach

Outcome

Is It Navajo? Accurate Language Detection for Endangered Athabaskan Languages (2025.naacl-short)

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Ivory Yang, Weicheng Ma, Chunhui Zhang, Soroush Vosoughi

Text Classification

Challenge:	Endangered languages are significantly underrepresented in contemporary language technologies, exacerbating the challenges of their preservation and revitalization.
Approach:	They propose a random forest classifier trained on Navajo and 20 erroneously suggested languages by Google's Language Identification tool.
Outcome:	The proposed classifier achieves near-perfect accuracy across other Athabaskan languages suggesting its potential for broader application.

What is it? Towards a Generalizable Native American Language Identification System (2025.naacl-srw)

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Ivory Yang, Weicheng Ma, Carlos Guerrero Alvarez, William Dinauer, Soroush Vosoughi

Information Extraction & Text Mining

Challenge:	Despite their cultural and historical significance, Native American languages remain unsupported by major commercial language identification systems.
Approach:	They propose to curate linguistic resources across all Native American languages for robust training and tailor data augmentation to generate synthetic yet linguistically coherent training samples.
Outcome:	The proposed system would be generalizable across all Native American languages . it would also generate coherent training samples for low-resource languages based on Plains Apache .

How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language (2022.acl-long)

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Shiyue Zhang, Ben Frey, Mohit Bansal

Challenge:	There are an estimated 6000 to 7000 spoken languages in the world, and at least 43% of them are endangered.
Approach:	They propose three principles that may help NLP practitioners foster mutual understanding and collaboration with language communities and three ways in which NLP can potentially assist in language education.
Outcome:	The proposed methods can be used to enrich Cherokee language resources with machine-in-the-loop processing and to provide language education.

Challenges of language technologies for the indigenous languages of the Americas (C18-1)

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Manuel Mager, Ximena Gutierrez-Vasques, Gerardo Sierra, Ivan Meza-Ruiz

Low-Resource NLP

Challenge:	Indigenous languages of the American continent are highly diverse, but have received little attention from the technological perspective.
Approach:	They review the research, the digital resources and the available NLP systems for indigenous languages of the American continent . they stress the need of developing language resources and NLP tools for these languages .
Outcome:	The authors review the research and the available NLP systems on indigenous languages of the Americas . they argue that the lack of resources and tools can have a negative impact on the communities which depend on these languages .

Revitalization of Indigenous Languages through Pre-processing and Neural Machine Translation: The case of Inuktitut (2020.coling-main)

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Tan Ngoc Le, Fatiha Sadat

Multilinguality

Challenge:	Indigenous languages have been considered low-resource and/or endangered . authors propose a method to revitalize the language spoken in northern canada .
Approach:	They propose to revitalize the Inuktitut language through pre-processing and neural machine translation . they propose to use this technique to perform morphological analysis and neural translation tasks .
Outcome:	The proposed approach improves the Inuktitut language compared to the state-of-the-art . the proposed approach is based on preprocessing and neural machine translation .

An Open Dataset and Model for Language Identification (2023.acl-short)

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Laurie Burchell, Alexandra Birch, Nikolay Bogoychev, Kenneth Heafield

Information Retrieval

Challenge:	Existing LID systems perform poorly on low-resource languages, causing 'representation washing', where the community is given a false view of the actual progress of low-source NLP.
Approach:	They propose a model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033% across 201 languages, outperforming previous work.
Outcome:	The proposed model outperforms existing models and datasets on 201 languages and a false positive rate of 0.033%.

FormosanBench: Benchmarking Low-Resource Austronesian Languages in the Era of Large Language Models (2025.findings-emnlp)

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Kaiying Kevin Lin, Hsi-Yu Chen, Haopeng Zhang

Language Models

Challenge:	Existing LLMs consistently underperform across all tasks, with 10-shot learning and fine-tuning offering only limited improvements.
Approach:	They introduce FormosanBench, a benchmark for evaluating LLMs on low-resource Austronesian languages.
Outcome:	The proposed benchmark covers three endangered Formosan languages: Atayal, Amis, and Paiwan . existing LLMs consistently underperform across all tasks, with 10-shot learning and fine-tuning offering only limited improvements.

One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia (2022.acl-long)

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Alham Fikri Aji, Genta Indra Winata, Fajri Koto, Samuel Cahyawijaya, Ade Romadhony, Rahmad Mahendra, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Timothy Baldwin, Jey Han Lau, Sebastian Ruder

Responsible & Trustworthy NLP

Challenge:	There are more than 700 languages spoken in Indonesia, equal to 10% of the world's languages, second only to Papua New Guinea.
Approach:	They focus on the languages spoken in Indonesia, the world's second most linguistically diverse nation, and the fourth most populous nation of the world.
Outcome:	The proposed model is based on the languages spoken in Indonesia, the world's second-most linguistically diverse nation, with 273 million people spread over 17,508 islands.

NLP Progress in Indigenous Latin American Languages (2024.naacl-long)

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Atnafu Tonja, Fazlourrahman Balouchzahi, Sabur Butt, Olga Kolesnikova, Hector Ceballos, Alexander Gelbukh, Thamar Solorio

Responsible & Trustworthy NLP

Challenge:	a new study examines the marginalization of indigenous languages in the face of rapid technological advancements.
Approach:	They highlight the cultural richness of indigenous languages and the risk they face of being overlooked in the realm of natural language processing.
Outcome:	The authors highlight the cultural richness of indigenous languages and their risk of being overlooked in the realm of natural language processing.

Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus (2020.coling-main)

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Isaac Caswell, Theresa Breiner, Daan van Esch, Ankur Bapna

Multilinguality

Challenge:	Large text corpora are increasingly important for a wide variety of NLP tasks.
Approach:	They propose to train automatic language identification models on up to 1,629 languages . they find that human-judged accuracy for web-crawl text corpora is only around 5% for many lower-resource languages.
Outcome:	The proposed models achieve over 90% average F1 on 1,629 languages . human-judged accuracy for web-crawl text corpora is only around 5% for many lower-resource languages - suggesting a need for more robust evaluation.