2021
EMNLP
EMNLP 2021
HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task
Abstract
AbstractThis paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task. We participate in the Russian-to-Chinese task under the constrained condition. We use Transformer architecture and obtain the best performance via a variant with larger parameter sizes. We perform detailed data pre-processing and filtering on the provided large-scale bilingual data. Several strategies are used to train our models, such as Multilingual Translation, Back Translation, Forward Translation, Data Denoising, Average Checkpoint, Ensemble, Fine-tuning, etc. Our system obtains 32.5 BLEU on the dev set and 27.7 BLEU on the test set, the highest score among all submissions.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
🐝
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
Zongyao Li
,
Daimeng Wei
,
Hengchao Shang
,
Xiaoyu Chen
,
Zhanglin Wu
,
Zhengzhe Yu
,
Jiaxin Guo
,
Minghan Wang
,
Lizhi Lei
,
Min Zhang
,
Hao Yang
,
Ying Qin
Topics
Deep Learning > Architectures > Transformers
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Generation > Machine Translation
Machine Learning > Learning Types > Multi-Modal Learning
Deep Learning > Models > Transformers
Deep Learning > Learning Types > Multi-Lingual Learning