@inproceedings{yang-etal-2024-exploring-traditional,
    title = "Exploring the Traditional {NMT} Model and Large Language Model for Chat Translation",
    author = "Yang, Jinlong  and
      Shang, Hengchao  and
      Wei, Daimeng  and
      Guo, Jiaxin  and
      Li, Zongyao  and
      Wu, Zhanglin  and
      Rao, Zhiqiang  and
      Li, Shaojun  and
      Xie, Yuhao  and
      Luo, Yuanchang  and
      Jiawei, Zheng  and
      Wei, Bin  and
      Yang, Hao",
    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/jlcl-multiple-ingestion/2024.wmt-1.105/",
    doi = "10.18653/v1/2024.wmt-1.105",
    pages = "1031--1037",
    abstract = "This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English{\ensuremath{\leftrightarrow}}Germany (en-de) bidirection. The experiments involved fine-tuning models using chat data and exploring various strategies, including Minimum Bayesian Risk (MBR) decoding and self-training. The results show significant performance improvements in certain directions, with the MBR self-training method achieving the best results. The Large Language Model also discusses the challenges and potential avenues for further research in the field of chat translation."
}