2018
NAACL
NAACL 2018
Pieces of Eight: 8-bit Neural Machine Translation
Abstract
AbstractNeural machine translation has achieved levels of fluency and adequacy that would have been surprising a short time ago. Output quality is extremely relevant for industry purposes, however it is equally important to produce results in the shortest time possible, mainly for latency-sensitive applications and to control cloud hosting costs. In this paper we show the effectiveness of translating with 8-bit quantization for models that have been trained using 32-bit floating point values. Results show that 8-bit translation makes a non-negligible impact in terms of speed with no degradation in accuracy and adequacy.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Trend Setter
— Efficient Computing
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Keyword Pioneer
— inference speed
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Hot Topic Early Bird
— model quantization
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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, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Application Areas > Efficient Computing
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Generation > Machine Translation
Artificial Intelligence > Core AI > Efficient Computing
Deep Learning > Optimization & Theory > Model Compression