Harnessing the Power of Artificial Intelligence to Vitalize Endangered Indigenous Languages: Technologies and Experiences
<|reference_start|>Harnessing the Power of Artificial Intelligence to Vitalize Endangered Indigenous Languages: Technologies and Experiences: Since 2022 we have been exploring application areas and technologies in which Artificial Intelligence (AI) and modern Natural Language Processing (NLP), such as Large Language Models (LLMs), can be employed to foster the usage and facilitate the documentation of Indigenous languages which are in danger of disappearing. We start by discussing the decreasing diversity of languages in the world and how working with Indigenous languages poses unique ethical challenges for AI and NLP. To address those challenges, we propose an alternative development AI cycle based on community engagement and usage. Then, we report encouraging results in the development of high-quality machine learning translators for Indigenous languages by fine-tuning state-of-the-art (SOTA) translators with tiny amounts of data and discuss how to avoid some common pitfalls in the process. We also present prototypes we have built in projects done in 2023 and 2024 with Indigenous communities in Brazil, aimed at facilitating writing, and discuss the development of Indigenous Language Models (ILMs) as a replicable and scalable way to create spell-checkers, next-word predictors, and similar tools. Finally, we discuss how we envision a future for language documentation where dying languages are preserved as interactive language models.<|reference_end|>
@article{pinhanez2024harnessing,
    title={Harnessing the Power of Artificial Intelligence to Vitalize Endangered
  Indigenous Languages: Technologies and Experiences},
    author={Claudio Pinhanez and Paulo Cavalin and Luciana Storto and Thomas
  Finbow and Alexander Cobbinah and Julio Nogima and Marisa Vasconcelos and
  Pedro Domingues and Priscila de Souza Mizukami and Nicole Grell and Majo'i
  Gongora and Isabel Gonc{c}alves},
    journal={arXiv preprint arXiv:2407.12620},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12620},
    primaryClass={cs.CL cs.AI}
}