Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW-TSC’s Submission to the WMT23 General MT Shared Task

Zhanglin Wu, Daimeng Wei, Zongyao Li, Zhengzhe Yu, Shaojun Li, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Yuhao Xie, Lizhi Lei, Hao Yang, Yanfei Jiang


Abstract
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT23 general machine translation (MT) shared task, in which we participate in Chinese↔English (zh↔en) language pair. We use Transformer architecture and obtain the best performance via a variant with larger parameter size. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. We mainly use model enhancement strategies, including Regularized Dropout, Bidirectional Training, Data Diversification, Forward Translation, Back Translation, Alternated Training, Curriculum Learning and Transductive Ensemble Learning. Our submissions obtain competitive results in the final evaluation.
Anthology ID:
2023.wmt-1.16
Volume:
Proceedings of the Eighth Conference on Machine Translation
Month:
December
Year:
2023
Address:
Singapore
Editors:
Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
170–174
Language:
URL:
https://aclanthology.org/2023.wmt-1.16
DOI:
10.18653/v1/2023.wmt-1.16
Bibkey:
Cite (ACL):
Zhanglin Wu, Daimeng Wei, Zongyao Li, Zhengzhe Yu, Shaojun Li, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Yuhao Xie, Lizhi Lei, Hao Yang, and Yanfei Jiang. 2023. Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW-TSC’s Submission to the WMT23 General MT Shared Task. In Proceedings of the Eighth Conference on Machine Translation, pages 170–174, Singapore. Association for Computational Linguistics.
Cite (Informal):
Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW-TSC’s Submission to the WMT23 General MT Shared Task (Wu et al., WMT 2023)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.wmt-1.16.pdf