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2021 EMNLP EMNLP 2021

The University of Maryland, College Park Submission to Large-Scale Multilingual Shared Task at WMT 2021

Abstract

AbstractThis paper describes the system submitted to Large-Scale Multilingual Shared Task (Small Task #2) at WMT 2021. It is based on the massively multilingual open-source model FLORES101_MM100 model, with selective fine-tuning. Our best-performing system reported a 15.72 average BLEU score for the task.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — selective fine-tuning
🐝 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

Saptarashmi Bandyopadhyay , Tasnim Kabir , Zizhen Lian , Marine Carpuat

Topics

Deep Learning > Architectures > Transformers 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 Deep Learning > Learning Types > Fine-Tuning

Keywords

machine translation multilingual translation multilingual machine translation low-resource language bleu score selective fine-tuning massively multilingual model
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