@inproceedings{perez-etal-2026-polyglotql,
    title = "{P}olyglot{QL}: A Pipeline for Multilingual Text-to-{SPARQL} Dataset Generation",
    author = "Perez, Julio  and
      Barth, Fabio  and
      Rehm, Georg",
    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-1.531/",
    doi = "10.63317/5ow3k3fbz296",
    pages = "6674--6684",
    abstract = "We present PolyglotQL, an open-source ETL (Extract, Transform, Load) pipeline for systematically creating multilingual text-to-SPARQL datasets, along with an accompanying framework for evaluating text-to-SPARQL generation models. PolyglotQL provides an extensible and modular architecture that aggregates, normalizes, and augments heterogeneous question{--}SPARQL pairs from established text-to-SPARQL datasets. With this pipeline, we automatically construct a bilingual English{--}German dataset featuring contextualized entity and relationship mappings as well as automatically translated and aligned question pairs. We also conduct an empirical evaluation using two multilingual open large language models under two distinct contextualization settings. The results show consistent performance improvements when explicit grounding information is provided, highlighting the benefits of structured context in multilingual semantic parsing."
}