@inproceedings{wang-etal-2025-nyas,
    title = "{NYA}{'}s Offline Speech Translation System for {IWSLT} 2025",
    author = "Wang, Wenxuan  and
      Zhang, Yingxin  and
      Jin, Yifan  and
      Du, Binbin  and
      Li, Yuke",
    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-1.19/",
    pages = "206--211",
    ISBN = "979-8-89176-272-5",
    abstract = "This paper reports NYA{'}s submissions to the IWSLT 2025 Offline Speech Translation (ST) task. The task includes three translation directions: English to Chinese, German, and Arabic. In detail, we adopt a cascaded speech translation architecture comprising automatic speech recognition (ASR) and machine translation (MT) components to participate in the unconstrained training track. For the ASR model, we use the Whisper medium model. For the neural machine translation (NMT) model, the wider and deeper Transformer is adopted as the backbone model. Building upon last year{'}s work, we implement multiple techniques and strategies such as data augmentation, domain adaptation, and model ensemble to improve the translation quality of the NMT model. In addition, we adopt X-ALMA as the foundational LLM-based MT model, with domain-specific supervised fine-tuning applied to train and optimize our LLM-based MT model. Finally, by employing COMET-based Minimum Bayes Risk decoding to integrate and select translation candidates from both NMT and LLM-based MT systems, the translation quality of our ST system is significantly improved, and competitive results are obtained on the evaluation set."
}