华为用聊天数据微调模型，结合MBR与自训练，提升英德对话翻译效果。
Exploring the traditional NMT model and Large Language Model for chat translation
- 用聊天语料微调传统NMT与大语言模型，适配对话场景。
- MBR自训练方法在部分方向上实现显著性能提升。
- 适合关注对话翻译优化与大模型应用的研究者。
本文介绍了华为翻译服务研究中心（HW-TSC）在WMT24对话翻译共享任务（英↔德双向）中的参赛方案。实验基于聊天语料对模型进行微调，并探索了最小贝叶斯风险（MBR）解码和自训练等多种策略。结果表明，在特定方向上取得了显著的性能提升，其中MBR自训练方法表现最佳。同时，论文讨论了大语言模型在对话翻译中面临的挑战及未来研究方向。
原文摘要 · Abstract (English)
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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