This article introduces the submission status of the Translation into Low-Resource Languages of Spain task at (WMT 2024) by Huawei Translation Service Center (HW-TSC). We participated in three translation tasks: spanish to aragonese (es-arg), spanish to aranese (es-arn), and spanish to asturian (es-ast). For these three translation tasks, we use training strategies such as multilingual transfer, regularized dropout, forward translation and back translation, labse denoising, transduction ensemble learning and other strategies to neural machine translation (NMT) model based on training deep transformer-big architecture. By using these enhancement strategies, our submission achieved a competitive result in the final evaluation.

翻译：本文介绍了华为翻译服务中心（HW-TSC）在WMT 2024“西班牙低资源语言翻译任务”中的提交情况。我们参与了三个翻译任务：西班牙语到阿拉贡语（es-arg）、西班牙语到阿兰语（es-arn）以及西班牙语到阿斯图里亚斯语（es-ast）。针对这三个翻译任务，我们在基于深度Transformer-big架构的神经机器翻译（NMT）模型中，采用了多语言迁移、正则化丢弃、前向翻译与反向翻译、LaBSE去噪、转导集成学习等训练策略。通过运用这些增强策略，我们的提交在最终评估中取得了具有竞争力的结果。