
Artificial intelligence's role in the realm of endangered languages: Documentation and teaching
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Abstract
With numerous languages nearing extinction, the urgency to preserve endangered languages has become a prominent focus in the linguistic field. This paper delves into the transformative role of Artificial Intelligence (AI) in the domains of documentation and pedagogy for endangered languages, particularly highlighting its innovative applications and the associated challenges. It delves into how AI-powered tools reshape linguistic fieldwork, offering accelerated annotation, consistent data collection, and deeper analytical endeavors. Furthermore, this exploration highlights the potential of AI in revolutionizing the teaching of these languages, ushering in a new era marked by dynamic, scalable, and engaging learning experiences. While AI presents unparalleled efficiencies, its challenges, ranging from data scarcity to the looming digital divide, are addressed critically. As the digital age continues to evolve, merging AI’s capabilities with traditional linguistic approaches holds the promise of a more inclusive and comprehensive strategy to rejuvenate and preserve the world’s rich linguistic tapestry. This paper has summarized and provided an outlook on the research topic at hand.
Keywords
Endangered Languages, Artificial Intelligence (AI), Linguistic Fieldwork, Documentation, Pedagogy
[1]. Austin, P. K., & Sallabank, J. (2014). Endangered Languages: An Introduction. Oxford University Press.
[2]. Zhang, S., Frey, B., & Bansal, M. (2022). How can NLP help revitalize endangered languages? A case study and roadmap for the Cherokee language. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
[3]. Wittenburg, P., Brugman, H., & Russel, A. et al. (2006). “ELAN: A Professional Framework for Multimodality Research.” Proceedings of LREC.
[4]. Nivre, J., de Marneffe, M. C., & Ginter, F. et al. (2019). “Universal Dependencies 2.4.” LINDAT/CLARIN digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University.
[5]. Blachon, D., Hamlaoui, F., & Jacobson, O. et al. (2016). “Parallel Speech Collection for Under-Resourced Language Studies Using the Lig-Aikuma Mobile Device App.” Procedia Computer Science, 81, 61-66.
[6]. Neubig, G., Duh, K., & Sudoh, K. (2011). “Learning to Translate with Multiple Objectives.” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics.
[7]. Wang, A., Singh, A., & Michael, J. et al. (2018). “GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.” Proceedings of the 2018 EMNLP Conference.
[8]. Devlin, J., Chang, M. W., & Lee, K. et al. (2018). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” arXiv preprint arXiv:1810.04805.
[9]. Radford, A., Wu, J., & Child, R. et al. (2019). “Language Models are Unsupervised Multitask Learners.” OpenAI Blog.
[10]. Goldsmith, J., & O’Brien, D. (2006). “Learning Inflectional Classes.” Language, 82(3), 555-592.
[11]. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for Deep Learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.
[12]. Heilman, M., & Smith, N. A. (2010). “Good Question! Statistical Ranking for Question Generation.” Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics.
[13]. Burstein, J., Chodorow, M., & Leacock, C. (2015). “Automatic Essay Evaluation: The Criterion Online Writing Service.” AI Magazine, 27(3).
[14]. Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does gamification work? -- A literature review of empirical studies on Gamification. 2014 47th Hawaii International Conference on System Sciences.
[15]. Shermis, M. D., & Hamner, B. (2013). Automated Essay Scoring: A Cross-disciplinary Perspective. Routledge.
[16]. Tetreault, J. R., & Chodorow, M. (2008). “The Ups and Downs of Preposition Error Detection in ESL Writing.” Proceedings of Coling.
[17]. Warschauer, M. (2003). Technology and Social Inclusion: Rethinking the Digital Divide. MIT Press.
[18]. Dörnyei, Z., & Ushioda, E. (2011). Teaching and Researching Motivation (2nd ed.). Pearson Education.
Cite this article
Wang,L. (2024). Artificial intelligence's role in the realm of endangered languages: Documentation and teaching. Applied and Computational Engineering,48,123-129.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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