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.
Abstract (translated)
本文描述了华为翻译服务中心（HW-TSC）在英语到德国（en-de）双向交互对话数据集上参加WMT24聊天翻译共享任务的研究提交。实验过程中，使用了聊天数据对模型进行微调并探讨了各种策略，包括最小贝叶斯风险（MBR）解码和自训练。结果表明，在某些方向上取得了显著的性能提升，其中MBR自训练方法取得了最佳结果。大型语言模型还讨论了聊天翻译领域进一步研究的挑战和潜在方向。
URL
https://arxiv.org/abs/2409.16331
