@inproceedings{vivel-couso-etal-2026-meteogaleus,
    title = "{M}eteo{G}al{E}us: An {I}berian Multilingual Weather Dataset in {G}alician, Euskera, and {S}panish",
    author = "Vivel-Couso, Ainhoa  and
      Pramata, Nella Zabrina  and
      Robredo, David  and
      Soroa, Aitor  and
      Alonso-Moral, Jose Maria",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.lrec-1.777/",
    doi = "10.63317/29v6u9pgo67x",
    pages = "9905--9919",
    abstract = "This paper introduces MeteoGalEus, a multilingual weather dataset that combines meteorological observations from two Spanish regional agencies, Euskalmet and MeteoGalicia. The dataset contains daily records spanning 4 years and 6 months, with aligned observations for both sources. MeteoGalEus captures key meteorological variables including temperature, wind and state of the sky. The dataset is provided in a structured format, facilitating data analysis and integration, with textual forecasts available in the official languages for each region (i.e., Galician and Spanish for MeteoGalicia; Euskera and Spanish for Euskalmet). By merging and harmonizing data from two regional agencies, MeteoGalEus is a unique resource for cross-regional weather analysis and multilingual climate studies. This dataset is suited for tasks requiring high-quality, aligned, and standardized weather data across multiple languages and regions. We conducted baseline experiments using LLaMA-based models in both zero-shot and fine-tuned settings to illustrate the use of MeteoGalEus for natural language generation (NLG). Fine-tuning led to consistent improvements across all metrics, with BERTScore increasing from 0.68 to 0.79, ROUGE from 0.20 to 0.35, and BLEU from 0.02 to 0.17 in the best-performing model. The experiments show how MeteoGalEus can be taken as a benchmark for multilingual and cross-regional NLG tasks."
}