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        <title>Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary</title>
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        <namePart type="given">Xing</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/P17-2091</identifier>
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%0 Conference Proceedings
%T Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary
%A Shi, Xing
%A Knight, Kevin
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F shi-knight-2017-speeding
%X 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.
%R 10.18653/v1/P17-2091
%U https://aclanthology.org/P17-2091
%U https://doi.org/10.18653/v1/P17-2091
%P 574-579

Markdown (Informal)

[Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary](https://aclanthology.org/P17-2091) (Shi & Knight, ACL 2017)

• Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary (Shi & Knight, ACL 2017)

ACL

• Xing Shi and Kevin Knight. 2017. Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 574–579, Vancouver, Canada. Association for Computational Linguistics.