Abstract
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.
Abstract (translated)
这篇文章介绍了华为翻译服务中心（HW-TSC）在2024年机器翻译国际会议（WMT 2024）上对将西班牙语翻译成低资源语言任务的提交状态。我们参与了三个翻译任务：西班牙语到阿拉贡语（es-arg）、西班牙语到阿拉尼亚语（es-arn）和西班牙语到 Asturian（es-ast）。为了这三个翻译任务，我们使用了一些训练策略，如多语言迁移、正则化失活、前向翻译和反向翻译、labse denoising、转换器集成学习等策略，对基于训练大架构的神经机器翻译（NMT）模型进行优化。通过使用这些增强策略，我们的提交在最终评估中获得了竞争力的结果。
URL
https://arxiv.org/abs/2409.15924
