A Unified Statistical Model for Generalized Translation Memory System

We introduced, for Translation Memory System, a statistical framework, which unifies the different phases in a Translation Memory System by letting them constrain each other, and enables Translation Memory System a statistical qualification. Compared to traditional Translation Memory Systems, our model operates at a fine grained sub-sentential level such that it improves the translation coverage. Compared with other approaches that exploit sub-sentential benefits, it unifies the processes of source string segmentation, best example selection, and translation generation by making them constrain each other via the statistical confidence of each step. We realized this framework into a prototype system. Compared with an existing product Translation Memory System, our system exhibits obviously better performance in the "assistant quality metric" and gains improvements in the range of 26.3% to 55.1% in the "translation efficiency metric". 1 This work was finished while the authors were working at Microsoft Research Asia (MSRA).