AI-powered Translation Technologies for Low-Resource Languages
How AI could shape the future of language preservation and dissemination and what it means for the workers and bearers of these heritages
KEY HIGHLIGHTS
AI-powered translation tools can support documentation and transmission of low-resource languages
The trend is driven by advancements in natural language processing (NLP), globalization, and decolonization movements.
Community-led, culturally sensitive approaches can contribute addressing data and ethical gaps
The Role of Language
Language is the lifeblood of cultural identity, memory, and shared knowledge. As languages face accelerating endangerment worldwide, a clear emerging trend is the growing use of AI-powered translation technologies to preserve, revitalize, and transmit oral traditions and expressions by widening communication, documentation, and intergenerational transmission capacities.
The trend manifests in the growing deployment of AI-powered tools such as low-resource language models (LrLMs), automatic speech recognition (ASR), real-time speech-to-speech translation, and localized interfaces optimized for indigenous and endangered languages.
The Role of Language
Language is the lifeblood of cultural identity, memory, and shared knowledge. As languages face accelerating endangerment worldwide, a clear emerging trend is the growing use of AI-powered translation technologies to preserve, revitalize, and transmit oral traditions and expressions by widening communication, documentation, and intergenerational transmission capacities.
The trend manifests in the growing deployment of AI-powered tools such as low-resource language models (LrLMs), automatic speech recognition (ASR), real-time speech-to-speech translation, and localized interfaces optimized for indigenous and endangered languages.
The Role of Language
Language is the lifeblood of cultural identity, memory, and shared knowledge. As languages face accelerating endangerment worldwide, a clear emerging trend is the growing use of AI-powered translation technologies to preserve, revitalize, and transmit oral traditions and expressions by widening communication, documentation, and intergenerational transmission capacities.
The trend manifests in the growing deployment of AI-powered tools such as low-resource language models (LrLMs), automatic speech recognition (ASR), real-time speech-to-speech translation, and localized interfaces optimized for indigenous and endangered languages.
OPEN CHALLENGES
Data scarcity and accessibility
High-quality, digitized language resources are limited, while marginalized communities often face uneven access to digital infrastructure, constraining AI development and equitable participation.
Cultural and ethical risks
AI deployment can raise ethical concerns, including cultural appropriation, data sovereignty, and the commodification of living heritage without proper community oversight.
Technological limitations
Current AI struggles with dialectal variation, oral traditions, and nuanced meanings, leading to potential inaccuracies or mistranslations that distort cultural heritage.
Representitiveness Gap
Language experts and heritage bearers are often underrepresented in technology governance, reducing opportunities for informed decision-making and culturally sensitive AI development.
CALLS TO ACTION
Engage as active partners in data collection, linguistic validation, and cultural contextualization.
Develop translation skills integrated with digital literacy to interface effectively with technology.
Collaborate with AI developers to shape culturally grounded algorithms and models.
Advocate for policies that respect indigenous data sovereignty and promote equitable tech access.
Key Technologies
Key Skills
ILLUSTRATIVE CASES FROM THE WEB
Hi! I’m an emerging case study from the web
di Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, Monojit Choudhury
RESEARCH
2024
Attention is all low-resource languages need
D. Poupard
Translation Studies Journal
2024
Teaching Large Language Models to Translate on Low-resource Languages with Textbook Prompting
P. Guo, Y. Ren, Y. Hu, Y. Li, J. Zhang, X. Zhang, H. Huang
International Conference on Language Resources and Evaluation
2025
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
T. Zhong, Z. Yang, Z. Liu, R. Zhang, Y. Liu, H. Sun, Y. Pan, Y. Li, Y. Zhou, H. Jiang, J. Chen, T. Liu
arXiv
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