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2021 EMNLP EMNLP 2021

NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation

Abstract

AbstractThis paper describes the NoahNMT system submitted to the WMT 2021 shared task of Very Low Resource Supervised Machine Translation. The system is a standard Transformer model equipped with our recent technique of dual transfer. It also employs widely used techniques that are known to be helpful for neural machine translation, including iterative back-translation, selected finetuning, and ensemble. The final submission achieves the top BLEU for three translation directions.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — dual transfer
🐝 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

Meng Zhang , Minghao Wu , Pengfei Li , Liangyou Li , Qun Liu

Topics

Deep Learning > Architectures > Transformers Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Paradigms > Transfer Learning Machine Learning > Learning Types > Transfer Learning Natural Language Processing > Generation > Machine Translation Deep Learning > Learning Types > Transfer Learning Deep Learning > Learning Types > Multi-Task Learning

Keywords

transfer learning ensemble learning neural machine translation model ensemble transformer model iterative back-translation dual transfer very low resource selected fine-tuning
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