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Seeding Statistical Machine Translation with Translation Memory Output through Tree-Based Structural Alignment
2010
Workshop on Syntax, Semantics and Structure in Statistical Translation
With the steadily increasing demand for high-quality translation, the localisation industry is constantly searching for technologies that would increase translator throughput, with the current focus on the use of high-quality Statistical Machine Translation (SMT) as a supplement to the established Translation Memory (TM) technology. In this paper we present a novel modular approach that utilises state-of-the-art sub-tree alignment to pick out pre-translated segments from a TM match and seed
dblp:conf/ssst/ZhechevG10
fatcat:nwdp2f2qqrfhtbyqurwc4njm2y