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2021 EMNLP EMNLP 2021

EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT

Abstract

AbstractWe describe the EdinSaar submission to the shared task of Multilingual Low-Resource Translation for North Germanic Languages at the Sixth Conference on Machine Translation (WMT2021). We submit multilingual translation models for translations to/from Icelandic (is), Norwegian-Bokmal (nb), and Swedish (sv). We employ various experimental approaches, including multilingual pre-training, back-translation, fine-tuning, and ensembling. In most translation directions, our models outperform other submitted systems.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — north germanic language
🐝 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

Svetlana Tchistiakova , Jesujoba Alabi , Koel Dutta Chowdhury , Sourav Dutta , Dana Ruiter

Topics

Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Types > Transfer Learning Natural Language Processing > Generation > Machine Translation Deep Learning > Learning Types > Multi-Modal Learning Machine Learning > Learning Types > Multi-Lingual Learning

Keywords

multilingual translation neural machine translation low-resource language model ensembling multilingual model multilingual pre-training north germanic language
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