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

Autor:	Luo, Yuanchang, Wu, Zhanglin, Wei, Daimeng, Shang, Hengchao, Li, Zongyao, Guo, Jiaxin, Rao, Zhiqiang, Li, Shaojun, Yang, Jinlong, Xie, Yuhao, Wei, Jiawei Zheng Bin, Yang, Hao
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages,wmt24. arXiv admin note: substantial text overlap with arXiv:2409.14842; text overlap with arXiv:2409.14800
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.15924 Zobrazit plný text záznamu View this record from Arxiv