Javier Aula-Blasco


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Building a Data Infrastructure for a Mid-Resource Language: The Case of Catalan
Aitor Gonzalez-Agirre | Montserrat Marimon | Carlos Rodriguez-Penagos | Javier Aula-Blasco | Irene Baucells | Carme Armentano-Oller | Jorge Palomar-Giner | Baybars Kulebi | Marta Villegas
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Current LLM-based applications are becoming steadily available for everyone with a reliable access to technology and the internet. These applications offer benefits to their users that leave those without access to them at a serious disadvantage. Given the vastly large amount of data needed to train LLMs, the gap between languages with access to such quantity of data and those without it is currently larger than ever. Aimed at saving this gap, the Aina Project was created to provide Catalan with the necessary resources to keep being relevant in the context of AI/NLP applications based on LLMs. We thus present a set of strategies to consider when improving technology support for a mid- or low-resource language, specially addressing sustainability of high-quality data acquisition and the challenges involved in the process. We also introduce a large amount of new annotated data for Catalan. Our hope is that those interested in replicating this work for another language can learn from what worked for us, the challenges that we faced, and the sometimes disheartening truth of working with mid- and low-resource languages.