2023
EMNLP
EMNLP 2023
HW-TSC’s Submissions to the WMT23 Discourse-Level Literary Translation Shared Task
Abstract
AbstractThis paper introduces HW-TSC’s submission to the WMT23 Discourse-Level Literary Translation shared task. We use standard sentence-level transformer as a baseline, and perform domain adaptation and discourse modeling to enhance discourse-level capabilities. Regarding domain adaptation, we employ Back-Translation, Forward-Translation and Data Diversification. For discourse modeling, we apply strategies such as Multi-resolutional Document-to-Document Translation and TrAining Data Augmentation.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
🐝
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
Yuhao Xie
,
Zongyao Li
,
Zhanglin Wu
,
Daimeng Wei
,
Xiaoyu Chen
,
Zhiqiang Rao
,
Shaojun Li
,
Hengchao Shang
,
Jiaxin Guo
,
Lizhi Lei
,
Hao Yang
,
Yanfei Jiang