Learning Translation Templates for Closely Related Languages

Many researchers have worked on example-based machine translation and different techniques have been investigated in the area. In literature, a method of using translation templates learned from bilingual example pairs was proposed. The paper investigates the possibility of applying the same idea for close languages where word order is preserved. In addition to applying the original algorithm for example pairs, we believe that the similarities between the translated sentences may always be learned as atomic translations. Since the word order is almost always preserved, there is no need to have any previous knowledge to identify the corresponding differences. The paper concludes that applying this method for close languages may improve the performance of the system.