Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain

• URL: http://arxiv.org/abs/2409.15924v2
• Date: Sun, 29 Sep 2024 09:15:42 GMT
• Title: Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain
• Authors: Yuanchang Luo, Zhanglin Wu, Daimeng Wei, Hengchao Shang, Zongyao Li, Jiaxin Guo, Zhiqiang Rao, Shaojun Li, Jinlong Yang, Yuhao Xie, Jiawei Zheng Bin Wei, Hao Yang,
• Abstract summary: 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, ensemble learning and other strategies to neural machine translation (NMT) model based on training deep transformer-big architecture.
• Score: 9.28989997114014
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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