Document-Specific Statistical Machine Translation for Improving Human Translation Productivity

Salim Roukos, Abraham Ittycheriah, Jian-Ming Xu

Abstract: We present two long term studies of the productivity of human translators by augmenting an existing Translation Memory system with Document-Specific Statistical Machine Translation. While the MT Post-Editing approach represents a significant change to the current practice of human translation, the two studies demonstrate a significant increase in the productivity of human translators, on the order of about 50% in the first study and of 68% in the second study conducted a year later. Both studies used a pool of 15 translators and concentrated on English-Spanish translation of IBM content in a production Translation Services Center.