2021
EMNLP
EMNLP 2021
HW-TSC’s Submissions to the WMT21 Biomedical Translation Task
Abstract
AbstractThis paper describes the submission of Huawei Translation Service Center (HW-TSC) to WMT21 biomedical translation task in two language pairs: Chinese↔English and German↔English (Our registered team name is HuaweiTSC). Technical details are introduced in this paper, including model framework, data pre-processing method and model enhancement strategies. In addition, using the wmt20 OK-aligned biomedical test set, we compare and analyze system performances under different strategies. On WMT21 biomedical translation task, Our systems in English→Chinese and English→German directions get the highest BLEU scores among all submissions according to the official evaluation results.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
🧭
Keyword Pioneer
— model enhancement
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Hao Yang
,
Zhanglin Wu
,
Zhengzhe Yu
,
Xiaoyu Chen
,
Daimeng Wei
,
Zongyao Li
,
Hengchao Shang
,
Minghan Wang
,
Jiaxin Guo
,
Lizhi Lei
,
Chuanfei Xu
,
Min Zhang
,
Ying Qin
Topics
Deep Learning > Architectures > Transformers
Natural Language Processing > Applications > Machine Translation
Machine Learning > Learning Types > Transfer Learning
Natural Language Processing > Generation > Machine Translation
Deep Learning > Models > Transformers
Deep Learning > Learning Types > Transfer Learning