@inproceedings{mondello-etal-2025-using,
    title = "Using {AI} Tools in Multimedia Localization Workflows: a Productivity Evaluation",
    author = "Mondello, Ashley  and
      Cini, Romina  and
      Rasane, Sahil  and
      Karakanta, Alina  and
      Casanellas, Laura",
    editor = {Bouillon, Pierrette  and
      Gerlach, Johanna  and
      Girletti, Sabrina  and
      Volkart, Lise  and
      Rubino, Raphael  and
      Sennrich, Rico  and
      L{\"a}ubli, Samuel  and
      Volk, Martin  and
      Espl{\`a}-Gomis, Miquel  and
      Vandeghinste, Vincent  and
      Moniz, Helena  and
      Szoc, Sara},
    booktitle = "Proceedings of Machine Translation Summit XX: Volume 2",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.mtsummit-2.1/",
    pages = "1--7",
    ISBN = "978-2-9701897-1-8",
    abstract = "Multimedia localization workflows are inherently complex, and the demand for localized content continues to grow. This demand has attracted Language Service Providers (LSPs) to expand their activities into multimedia localization, offering subtitling and voice-over services. While a wide array of AI tools is available for these tasks, their value in increasing productivity in multimedia workflows for LSPs remains uncertain. This study evaluates the productivity, quality, cost, and time efficiency of three multimedia localization workflows, each incorporating varying levels of AI automation. Our findings indicate that workflows merely replacing human vendors with AI tools may result in quality degradation without justifying the productivity gains. In contrast, integrated workflows using specialized tools enhance productivity while maintaining quality, despite requiring additional training and adjustments to established practices."
}