Fast, Scalable Phrase-Based SMT Decoding

Hieu Hoang, Nikolay Bogoychev, Lane Schwartz, Marcin Junczys-Dowmunt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMT Researchers' Track
EditorsSpence Green, Lane Schwartz
PublisherAssociation for Machine Translation in the Americas
Pages40-52
Number of pages13
ISBN (Electronic)9780000000002
StatePublished - Jan 1 2016
Event12th Conference of the Association for Machine Translation in the Americas, AMTA 2016 - Austin, United States
Duration: Oct 28 2016Nov 1 2016

Publication series

NameProceedings - AMTA 2016: 12th Conference of the Association for Machine Translation in the Americas
Volume1

Conference

Conference12th Conference of the Association for Machine Translation in the Americas, AMTA 2016
CountryUnited States
CityAustin
Period10/28/1611/1/16

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software
  • Language and Linguistics

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  • Cite this

    Hoang, H., Bogoychev, N., Schwartz, L., & Junczys-Dowmunt, M. (2016). Fast, Scalable Phrase-Based SMT Decoding. In S. Green, & L. Schwartz (Eds.), MT Researchers' Track (pp. 40-52). (Proceedings - AMTA 2016: 12th Conference of the Association for Machine Translation in the Americas; Vol. 1). Association for Machine Translation in the Americas.