Huawei’s Submissions to the WMT20 Biomedical Translation Task

Wei Peng, Jianfeng Liu, Minghan Wang, Liangyou Li, Xupeng Meng, Hao Yang, Qun Liu


Abstract
This paper describes Huawei’s submissions to the WMT20 biomedical translation shared task. Apart from experimenting with finetuning on domain-specific bitexts, we explore effects of in-domain dictionaries on enhancing cross-domain neural machine translation performance. We utilize a transfer learning strategy through pre-trained machine translation models and extensive scope of engineering endeavors. Four of our ten submissions achieve state-of-the-art performance according to the official automatic evaluation results, namely translation directions on English<->French, English->German and English->Italian.
Anthology ID:
2020.wmt-1.93
Volume:
Proceedings of the Fifth Conference on Machine Translation
Month:
November
Year:
2020
Address:
Online
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
857–861
Language:
URL:
https://aclanthology.org/2020.wmt-1.93
DOI:
Bibkey:
Cite (ACL):
Wei Peng, Jianfeng Liu, Minghan Wang, Liangyou Li, Xupeng Meng, Hao Yang, and Qun Liu. 2020. Huawei’s Submissions to the WMT20 Biomedical Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 857–861, Online. Association for Computational Linguistics.
Cite (Informal):
Huawei’s Submissions to the WMT20 Biomedical Translation Task (Peng et al., WMT 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.wmt-1.93.pdf
Video:
 https://slideslive.com/38939576