@inproceedings{bahar-etal-2023-speech,
    title = "Speech Translation with Style: {A}pp{T}ek{'}s Submissions to the {IWSLT} Subtitling and Formality Tracks in 2023",
    author = {Bahar, Parnia  and
      Wilken, Patrick  and
      Iranzo-S{\'a}nchez, Javier  and
      Di Gangi, Mattia  and
      Matusov, Evgeny  and
      T{\"u}ske, Zolt{\'a}n},
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.iwslt-1.22",
    doi = "10.18653/v1/2023.iwslt-1.22",
    pages = "251--260",
    abstract = "AppTek participated in the subtitling and formality tracks of the IWSLT 2023 evaluation. This paper describes the details of our subtitling pipeline - speech segmentation, speech recognition, punctuation prediction and inverse text normalization, text machine translation and direct speech-to-text translation, intelligent line segmentation - and how we make use of the provided subtitling-specific data in training and fine-tuning. The evaluation results show that our final submissions are competitive, in particular outperforming the submissions by other participants by 5{\%} absolute as measured by the SubER subtitle quality metric. For the formality track, we participate with our En-Ru and En-Pt production models, which support formality control via prefix tokens. Except for informal Portuguese, we achieve near perfect formality level accuracy while at the same time offering high general translation quality.",
}

AppTek participated in the subtitling and formality tracks of the IWSLT 2023 evaluation. This paper describes the details of our subtitling pipeline - speech segmentation, speech recognition, punctuation prediction and inverse text normalization, text machine translation and direct speech-to-text translation, intelligent line segmentation - and how we make use of the provided subtitling-specific data in training and fine-tuning. The evaluation results show that our final submissions are competitive, in particular outperforming the submissions by other participants by 5{\%} absolute as measured by the SubER subtitle quality metric. For the formality track, we participate with our En-Ru and En-Pt production models, which support formality control via prefix tokens. Except for informal Portuguese, we achieve near perfect formality level accuracy while at the same time offering high general translation quality.
Bahar, Parnia Wilken, Patrick Iranzo-S{\'a}nchez, Javier Di Gangi, Mattia Matusov, Evgeny T{\"u}ske, Zolt{\'a}n
Speech Translation with Style: AppTek's Submissions to the IWSLT Subtitling and Formality Tracks in 2023