MessIRve: A Large-Scale Spanish Information Retrieval Dataset
This addresses the problem of limited information access tools for Spanish speakers, though it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the lack of large-scale Spanish information retrieval datasets by introducing MessIRve, a dataset with almost 700,000 queries from Google's autocomplete API and relevant documents from Wikipedia, which supports diverse Spanish-speaking regions and topics.
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.