2021
EMNLP
EMNLP 2021
The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
Abstract
AbstractThis paper describes the Volctrans’ submission to the WMT21 news translation shared task for German->English translation. We build a parallel (i.e., non-autoregressive) translation system using the Glancing Transformer, which enables fast and accurate parallel decoding in contrast to the currently prevailing autoregressive models. To the best of our knowledge, this is the first parallel translation system that can be scaled to such a practical scenario like WMT competition. More importantly, our parallel translation system achieves the best BLEU score (35.0) on German->English translation task, outperforming all strong autoregressive counterparts.
🌉
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, Security & Privacy, Speech & Audio
Authors
Lihua Qian
,
Yi Zhou
,
Zaixiang Zheng
,
Yaoming Zhu
,
Zehui Lin
,
Jiangtao Feng
,
Shanbo Cheng
,
Lei Li
,
Mingxuan Wang
,
Hao Zhou