Abstract
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.
Abstract (translated)
本文提交了华为翻译服务中心（HW-TSC）在2024年第三届中国机器翻译会议（CCMT 2024）上对机器翻译任务的提交。我们参与了双语机器翻译任务和多领域机器翻译任务。为了训练这两种翻译任务，我们采用了诸如正则化dropout、双向训练、数据多样性、前向翻译、反向翻译、交替训练、课程学习以及转换器集成学习等训练策略，基于深度Transformer-big架构训练神经机器翻译（NMT）模型。 此外，为了探讨大型语言模型（LLM）是否可以帮助提高神经机器翻译（NMT）系统的翻译质量，我们使用有监督的微调来训练llama2-13b作为自动后编辑（APE）模型，以提高NMT模型在多领域机器翻译任务上的翻译结果。通过使用这些跳涨策略，我们的提交在最终评估中实现了竞争力的结果。
URL
https://arxiv.org/abs/2409.14842
