As the smallest meaning-bearing elements of the languages which have rich morphology information, morphemes are often integrated into state-of-the-art statistical machine translation to improve translation quality. The paper proposes an approach which novelly uses morphemes as pivot language in a chained machine translation system. A machine translation based method is used therein to find the mapping relations between morphemes and words. Experiments show the effectiveness of our approach, achieving 18.6 percent increase in BLEU score over the baseline phrase-based machine translation system.
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