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arXiv · 2409.16331

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

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
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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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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. 2024-09-24. Exploring the traditional NMT model and Large Language Model for chat translation. https://arxiv.org/abs/2409.16331

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