This paper presents the submission of Huawei Translation Services Center (HW-TSC) to machine translation tasks of the 20th China Conference on Machine Translation (CCMT 2024). We participate in the bilingual machine translation task and multi-domain machine translation task. For these two translation tasks, we use training strategies such as regularized dropout, bidirectional training, data diversification, forward translation, back translation, alternated training, curriculum learning, and transductive ensemble learning to train neural machine translation (NMT) models based on the deep Transformer-big architecture. Furthermore, to explore whether large language model (LLM) can help improve the translation quality of NMT systems, we use supervised fine-tuning to train llama2-13b as an Automatic post-editing (APE) model to improve the translation results of the NMT model on the multi-domain machine translation task. By using these plyometric strategies, our submission achieves a competitive result in the final evaluation.

翻译：本文介绍了华为翻译服务中心（HW-TSC）向第二十届全国机器翻译大会（CCMT 2024）机器翻译任务提交的系统。我们参与了双语机器翻译任务和多领域机器翻译任务。针对这两项翻译任务，我们采用了正则化丢弃、双向训练、数据多样化、前向翻译、反向翻译、交替训练、课程学习以及转导集成学习等训练策略，基于深层Transformer-big架构训练神经机器翻译模型。此外，为探究大语言模型能否帮助提升NMT系统的翻译质量，我们采用监督微调方法训练llama2-13b作为自动后编辑模型，以改进NMT模型在多领域机器翻译任务上的翻译结果。通过运用这些增强策略，我们的提交在最终评测中取得了具有竞争力的成绩。