Exploring the traditional NMT model and Large Language Model for chat translation
<|reference_start|>Exploring the traditional NMT model and Large Language Model for chat translation: This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English$\leftrightarrow$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.<|reference_end|>
@article{yang2024exploring,
    title={Exploring the traditional NMT model and Large Language Model for chat
  translation},
    author={Jinlong Yang, Hengchao Shang, Daimeng Wei, Jiaxin Guo, Zongyao Li,
  Zhanglin Wu, Zhiqiang Rao, Shaojun Li, Yuhao Xie, Yuanchang Luo, Jiawei
  Zheng, Bin Wei, Hao Yang},
    journal={arXiv preprint arXiv:2409.16331},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16331},
    primaryClass={cs.CL cs.AI}
}