@inproceedings{li-etal-2023-hw,
    title = "{HW}-{TSC} at {IWSLT}2023: Break the Quality Ceiling of Offline Track via Pre-Training and Domain Adaptation",
    author = "Li, Zongyao  and
      Wu, Zhanglin  and
      Rao, Zhiqiang  and
      YuHao, Xie  and
      JiaXin, Guo  and
      Wei, Daimeng  and
      Shang, Hengchao  and
      Minghan, Wang  and
      Chen, Xiaoyu  and
      Yu, Zhengzhe  and
      ShaoJun, Li  and
      LiZhi, Lei  and
      Yang, Hao",
    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-1.14",
    doi = "10.18653/v1/2023.iwslt-1.14",
    pages = "187--193",
    abstract = "This paper presents HW-TSC{'}s submissions to the IWSLT 2023 Offline Speech Translation task, including speech translation of talks from English to German, Chinese, and Japanese, respectively. We participate in all three conditions (constrained training, constrained with large language models training, and unconstrained training) with models of cascaded architectures. We use data enhancement, pre-training models and other means to improve the ASR quality, and R-Drop, deep model, domain data selection, etc. to improve the translation quality. Compared with last year{'}s best results, we achieve 2.1 BLEU improvement on the MuST-C English-German test set.",
}

This paper presents HW-TSC{'}s submissions to the IWSLT 2023 Offline Speech Translation task, including speech translation of talks from English to German, Chinese, and Japanese, respectively. We participate in all three conditions (constrained training, constrained with large language models training, and unconstrained training) with models of cascaded architectures. We use data enhancement, pre-training models and other means to improve the ASR quality, and R-Drop, deep model, domain data selection, etc. to improve the translation quality. Compared with last year{'}s best results, we achieve 2.1 BLEU improvement on the MuST-C English-German test set.
Li, Zongyao Wu, Zhanglin Rao, Zhiqiang YuHao, Xie JiaXin, Guo Wei, Daimeng Shang, Hengchao Minghan, Wang Chen, Xiaoyu Yu, Zhengzhe ShaoJun, Li LiZhi, Lei Yang, Hao
HW-TSC at IWSLT2023: Break the Quality Ceiling of Offline Track via Pre-Training and Domain Adaptation