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2022 EMNLP EMNLP 2022

HW-TSC Translation Systems for the WMT22 Chat Translation Task

Abstract

AbstractThis paper describes the submissions of Huawei Translation Services Center (HW-TSC) to WMT22 chat translation shared task on English-Germany (en-de) bidirection with results of zore-shot and few-shot tracks. We use the deep transformer architecture with a lager parameter size. Our submissions to the WMT21 News Translation task are used as the baselines. We adopt strategies such as back translation, forward translation, domain transfer, data selection, and noisy forward translation in task, and achieve competitive results on the development set. We also test the effectiveness of document translation on chat tasks. Due to the lack of chat data, the results on the development set show that it is not as effective as sentence-level translation models.

🌉 Interdisciplinary Bridge — Artificial Intelligence and 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

Jinlong Yang , Zongyao Li , Daimeng Wei , Hengchao Shang , Xiaoyu Chen , Zhengzhe Yu , Zhiqiang Rao , Shaojun Li , Zhanglin Wu , Yuhao Xie , Yuanchang Luo , Ting Zhu , Yanqing Zhao , Lizhi Lei , Hao Yang , Ying Qin

Topics

Artificial Intelligence > Learning Paradigms > Few-Shot Learning Machine Learning > Learning Types > Zero-Shot Learning Deep Learning > Architectures > Transformers Natural Language Processing > Applications > Machine Translation Deep Learning > Learning Types > Domain Adaptation

Keywords

zero-shot learning few-shot learning domain adaptation machine translation neural machine translation data selection back translation chat translation
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