@inproceedings{valentini-etal-2025-messirve,
    title = "{M}ess{IR}ve: A Large-Scale {S}panish Information Retrieval Dataset",
    author = "Valentini, Francisco  and
      Cotik, Viviana  and
      Furman, Dami{\'a}n  and
      Bercovich, Ivan  and
      Altszyler, Edgar  and
      P{\'e}rez, Juan Manuel",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1412/",
    pages = "27740--27757",
    ISBN = "979-8-89176-332-6",
    abstract = "Information retrieval (IR) is the task of finding relevant documents in response to a user query. Although Spanish is the second most spoken native language, there are few Spanish IR datasets, which limits the development of information access tools for Spanish speakers. We introduce MessIRve, a large-scale Spanish IR dataset with almost 700,000 queries from Google{'}s autocomplete API and relevant documents sourced from Wikipedia. MessIRve{'}s queries reflect diverse Spanish-speaking regions, unlike other datasets that are translated from English or do not consider dialectal variations. The large size of the dataset allows it to cover a wide variety of topics, unlike smaller datasets. We provide a comprehensive description of the dataset, comparisons with existing datasets, and baseline evaluations of prominent IR models. Our contributions aim to advance Spanish IR research and improve information access for Spanish speakers."
}