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• W2739738036 abstract "We speed up Neural Machine Translation (NMT) decoding by shrinking run-time target vocabulary. We experiment with two shrinking approaches: Locality Sensitive Hashing (LSH) and word alignments. Using the latter method, we get a 2x overall speed-up over a highly-optimized GPU implementation, without hurting BLEU. On certain low-resource language pairs, the same methods improve BLEU by 0.5 points. We also report a negative result for LSH on GPUs, due to relatively large overhead, though it was successful on CPUs. Compared with Locality Sensitive Hashing (LSH), decoding with word alignments is GPU-friendly, orthogonal to existing speedup methods and more robust across language pairs." @default.
• W2739738036 created "2017-08-08" @default.
• W2739738036 creator A5031163887 @default.
• W2739738036 creator A5041833974 @default.
• W2739738036 date "2017-01-01" @default.
• W2739738036 modified "2023-10-14" @default.
• W2739738036 title "Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary" @default.
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• W2739738036 doi "https://doi.org/10.18653/v1/p17-2091" @default.
• W2739738036 hasPublicationYear "2017" @default.
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