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2021 EMNLP EMNLP 2021

The University of Edinburgh’s English-German and English-Hausa Submissions to the WMT21 News Translation Task

Abstract

AbstractThis paper presents the University of Edinburgh’s constrained submissions of English-German and English-Hausa systems to the WMT 2021 shared task on news translation. We build En-De systems in three stages: corpus filtering, back-translation, and fine-tuning. For En-Ha we use an iterative back-translation approach on top of pre-trained En-De models and investigate vocabulary embedding mapping.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — vocabulary embedding mapping
🐝 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

Pinzhen Chen , Jindřich Helcl , Ulrich Germann , Laurie Burchell , Nikolay Bogoychev , Antonio Valerio Miceli Barone , Jonas Waldendorf , Alexandra Birch , Kenneth Heafield

Topics

Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Types > Transfer Learning Deep Learning > Learning Types > Self-Supervised Learning Deep Learning > Learning Types > Transfer Learning

Keywords

neural machine translation multilingual model corpus filtering vocabulary embedding mapping
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