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arxiv: 2409.16331 · v1 · pith:6WO667V2 · submitted 2024-09-24 · cs.CL · cs.AI

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
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Yuhao Xie Yuanchang Luo Jiawei Zheng Bin Wei Hao Yang
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classification cs.CL cs.AI
keywords chattranslationmodelexploringlanguagelargeresultsself-training
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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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