Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain
This work addresses translation challenges for low-resource languages like Aragonese, Aranese, and Asturian, but it is incremental as it builds on existing methods without major breakthroughs.
The paper tackled machine translation for low-resource languages of Spain by applying multilingual transfer and domain adaptation strategies to a transformer model, achieving competitive results in the WMT 2024 evaluation.
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.