@inproceedings{luger-etal-2025-building,
    title = "Building Data Infrastructure for Low-Resource Languages",
    author = "Luger, Sarah  and
      Mosquera-G{\'o}mez, Rafael  and
      Mi{\l}owski, Alex  and
      Vaughan, Thom  and
      Hincapie-Monsalve, Sara  and
      Ortiz Suarez, Pedro  and
      Bollacker, Kurt",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, U.S.A.",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/metadata-correction-jian-chen-ub/2025.loresmt-1.14/",
    doi = "10.18653/v1/2025.loresmt-1.14",
    pages = "154--160",
    ISBN = "979-8-89176-230-5",
    abstract = "The MLCommons Datasets Working Group presents a comprehensive initiative to advance the development and accessibility of artificial intelligence (AI) training and testing resources. This paper introduces three key projects aimed at addressing critical gaps in the AI data ecosystem: the Unsupervised People{'}s Speech Dataset, containing over 821,000 hours of speech across 89+ languages; a strategic collaboration with Common Crawl to enhance web crawling capabilities for low-resource languages; and a framework for knowledge graph extraction evaluation. By focusing on languages other than English (LOTE) and creating permissively licensed, high-quality datasets, these initiatives aim to democratize AI development and improve model performance across diverse linguistic contexts. This work represents a significant step toward more inclusive and capable AI systems that can serve global communities."
}