@inproceedings{kondo-etal-2024-nttsu,
    title = "{NTTSU} at {WMT}2024 General Translation Task",
    author = "Kondo, Minato  and
      Fukuda, Ryo  and
      Wang, Xiaotian  and
      Chousa, Katsuki  and
      Nishimura, Masato  and
      Buma, Kosei  and
      Kano, Takatomo  and
      Utsuro, Takehito",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.wmt-1.20/",
    doi = "10.18653/v1/2024.wmt-1.20",
    pages = "270--279",
    abstract = "The NTTSU team`s submission leverages several large language models developed through a training procedure that includes continual pre-training and supervised fine-tuning. For paragraph-level translation, we generated synthetic paragraph-aligned data and utilized this data for training.In the task of translating Japanese to Chinese, we particularly focused on the speech domain translation. Specifically, we built Whisper models for Japanese automatic speech recognition (ASR). We used YODAS dataset for Whisper training. Since this data contained many noisy data pairs, we combined the Whisper outputs using ROVER for polishing the transcriptions. Furthermore, to enhance the robustness of the translation model against errors in the transcriptions, we performed data augmentation by forward translation from audio, using both ASR and base translation models.To select the best translation from multiple hypotheses of the models, we applied Minimum Bayes Risk decoding + reranking, incorporating scores such as COMET-QE, COMET, and cosine similarity by LaBSE."
}