@inproceedings{petrick-etal-2025-appteks,
    title = "{A}pp{T}ek{'}s Automatic Speech Translation: Generating Accurate and Well-Readable Subtitles",
    author = "Petrick, Frithjof  and
      Wilken, Patrick  and
      Matusov, Evgeny  and
      Unai Rosell{\'o} Beneitez, Nahuel  and
      Beranek, Sarah",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Anastasopoulos, Antonis",
    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.iwslt-1.21/",
    pages = "222--231",
    ISBN = "979-8-89176-272-5",
    abstract = "We describe AppTek{'}s submission to the subtitling track of the IWSLT 2025 evaluation. We enhance our cascaded speech translation approach by adapting the ASR and the MT models on in-domain data. All components, including intermediate steps such as subtitle source language template creation and line segmentation, are optimized to ensure that the resulting target language subtitles respect the subtitling constraints not only on the number of characters per line and the number of lines in each subtitle block, but also with respect to the desired reading speed. AppTek{'}s machine translation with length control plays the key role in this process, effectively condensing subtitles to these constraints. Our experiments show that this condensation results in high-quality translations that convey the most important information, as measured by metrics such as BLEU or BLEURT, as well as the primary metric subtitle edit rate (SubER)."
}