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.
Hoang et al. REF re-examine the major components of phrase-based decoding and decoder implementation with specific accentuation on speed and versatility on multicore machines.
Fast, Scalable Phrase-Based SMT Decoding
ArXiv
ArXiv
Computer Science