2024
EMNLP
EMNLP 2024
Exploring the Traditional NMT Model and Large Language Model for Chat Translation
Abstract
AbstractThis paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English↔Germany (en-de) bidirection. The experiments involved fine-tuning models using chat data and exploring various strategies, including Minimum Bayesian Risk (MBR) decoding and self-training. The results show significant performance improvements in certain directions, with the MBR self-training method achieving the best results. The Large Language Model also discusses the challenges and potential avenues for further research in the field of chat translation.
🌉
Interdisciplinary Bridge
— 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
Jinlong Yang
,
Hengchao Shang
,
Daimeng Wei
,
Jiaxin Guo
,
Zongyao Li
,
Zhanglin Wu
,
Zhiqiang Rao
,
Shaojun Li
,
Yuhao Xie
,
Yuanchang Luo
,
Zheng Jiawei
,
Bin Wei
,
Hao Yang