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    <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 structured 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, and Asturian), 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.</abstract>
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%0 Conference Proceedings
%T AI-TraLow: AI-Driven Translation for Low-Resource Languages and Cultures
%A Oliver, Antoni
%A Melero, Maite
%A Sanchez-Martinez, Felipe
%A Sánchez-Cartagena, Víctor M.
%Y Claramunt, German Rigau
%Y Gamallo, Pablo
%Y Muñoz Guillena, Rafael
%Y Chiruzzo, Luis
%Y Martínez Cámara, Eugenio
%S Proceedings of LANLP: Bridging Ibero and Latin American NLP Communities
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F oliver-etal-2026-ai
%X 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 structured 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, and Asturian), 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.
%R 10.63317/32hkgizmdnhw
%U https://aclanthology.org/2026.lanlp-1.3/
%U https://doi.org/10.63317/32hkgizmdnhw
%P 15-20

Markdown (Informal)

[AI-TraLow: AI-Driven Translation for Low-Resource Languages and Cultures](https://aclanthology.org/2026.lanlp-1.3/) (Oliver et al., LANLP 2026)

• AI-TraLow: AI-Driven Translation for Low-Resource Languages and Cultures (Oliver et al., LANLP 2026)

ACL

• Antoni Oliver, Maite Melero, Felipe Sanchez-Martinez, and Víctor M. Sánchez-Cartagena. 2026. AI-TraLow: AI-Driven Translation for Low-Resource Languages and Cultures. In Proceedings of LANLP: Bridging Ibero and Latin American NLP Communities, pages 15–20, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).