2018
ACL
ACL 2018
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU
Abstract
AbstractWe present a system description of the OpenNMT Neural Machine Translation entry for the WNMT 2018 evaluation. In this work, we developed a heavily optimized NMT inference model targeting a high-performance CPU system. The final system uses a combination of four techniques, all of them lead to significant speed-ups in combination: (a) sequence distillation, (b) architecture modifications, (c) precomputation, particularly of vocabulary, and (d) CPU targeted quantization. This work achieves the fastest performance of the shared task, and led to the development of new features that have been integrated to OpenNMT and available to the community.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Trend Setter
— Model Compression
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Keyword Pioneer
— inference optimization
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Hot Topic Early Bird
— neural machine translation
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Deep Learning, Machine Learning, Natural Language Processing, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Model Compression
Machine Learning > Application Areas > Efficient Computing
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Generation > Machine Translation
Deep Learning > Optimization & Theory > Efficient Computing
Deep Learning > Learning Types > Model Compression