{"id":468933,"date":"2018-02-23T09:03:41","date_gmt":"2018-02-23T17:03:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=468933"},"modified":"2018-10-16T22:34:57","modified_gmt":"2018-10-17T05:34:57","slug":"fast-scalable-phrase-based-smt-decoding","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/fast-scalable-phrase-based-smt-decoding\/","title":{"rendered":"Fast, scalable phrase-based smt decoding"},"content":{"rendered":"
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.<\/p>\n
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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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