Year: 2026
Type: conference-paper
Abstract: In this paper, we present AI-TraLow, a project dedicated to advancing AI-driven translation for low-resource languages and cultures.The research is structured around three primary objectives: firstly, the development of advanced data curation techniques designed to refine parallel corpora and detect machine-generated content; secondly, the exploration of integrating linguistic resources-such as dictionaries and grammatical rules-directly into model prompts and fine-tuning techniques to enhance translation precision; and thirdly, the mitigation of hardware constraints through knowledge distillation to produce efficient models viable for standard desktop environments.By targeting specific linguistic groups, including Iberian varieties (Aranese, Aragonese, Asturian and Eonavian), Mayan languages, and languages of vulnerable migrant communities, AI-TraLow seeks to foster linguistic diversity and digital inclusion.Ultimately, this initiative delivers open-source tools and models that ensure cultural heritage is both preserved and accessible within the contemporary digital landscape.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7166592090