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arXiv · 1610.04265

Fast, Scalable Phrase-Based SMT Decoding

Hieu Hoang·Nikolay Bogoychev·Lane Schwartz·Marcin Junczys-Dowmunt
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Abstract

The utilization of statistical machine translation (SMT) has grown enormously over the last decade, many using open-source software developed by the NLP community. As commercial use has increased, there is need for software that is optimized for commercial requirements, in particular, fast phrase-based decoding and more efficient utilization of modern multicore servers. In this paper we re-examine the major components of phrase-based decoding and decoder implementation with particular emphasis on speed and scalability on multicore machines. The result is a drop-in replacement for the Moses decoder which is up to fifteen times faster and scales monotonically with the number of cores.

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Hieu Hoang, Nikolay Bogoychev, Lane Schwartz, Marcin Junczys-Dowmunt. 2016-10-18. Fast, Scalable Phrase-Based SMT Decoding. https://arxiv.org/abs/1610.04265

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