Fast Neural Machine Translation Implementation

Hieu Hoang, Tomasz Dwojak, Rihards Krislauks, Daniel Torregrosa, Kenneth Heafield


Abstract
This paper describes the submissions to the efficiency track for GPUs at the Workshop for Neural Machine Translation and Generation by members of the University of Edinburgh, Adam Mickiewicz University, Tilde and University of Alicante. We focus on efficient implementation of the recurrent deep-learning model as implemented in Amun, the fast inference engine for neural machine translation. We improve the performance with an efficient mini-batching algorithm, and by fusing the softmax operation with the k-best extraction algorithm. Submissions using Amun were first, second and third fastest in the GPU efficiency track.
Anthology ID:
W18-2714
Volume:
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | NGT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
116–121
Language:
URL:
https://www.aclweb.org/anthology/W18-2714
DOI:
10.18653/v1/W18-2714
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-2714.pdf