Exploring the traditional NMT model and Large Language Model for chat translation

Jinlong Yang, Hengchao Shang, Daimeng Wei, Jiaxin Guo, Zongyao Li +8 more
9/24/2024
cs.CLcs.AI

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Abstract

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.

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Cite this paper

@article{yang2024exploring,
  title  = {Exploring the traditional NMT model and Large Language Model for chat translation},
  author = {Jinlong Yang and Hengchao Shang and Daimeng Wei and Jiaxin Guo and Zongyao Li and Zhanglin Wu and Zhiqiang Rao and Shaojun Li and Yuhao Xie and Yuanchang Luo and Jiawei Zheng and Bin Wei and Hao Yang},
  year   = {2024},
  eprint = {2409.16331},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.16331v1}
}

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