2021
IJCNLP
IJCNLP 2021
How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task
Abstract
AbstractThis work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization. We train and evaluate large multilingual translation models using a single GPU for a maximum of 100 hours and get within 4-5 BLEU points of the top submission on the leaderboard. We also benchmark standard baselines on the PMI corpus and re-discover well-known shortcomings of translation systems and metrics.
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The Questioner
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— model optimization
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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