The Effect of Translationese on Tuning for Statistical Machine Translation

Abstract

We explore how the translation direction in the tuning set used for statistical machine translation affects the translation results. We explore this issue for three language pairs. While the results on different metrics are somewhat conflicting, using tuning data translated in the same direction as the translation systems tends to give the best length ratio and Meteor scores for all language pairs. This tendency is confirmed in a small human evaluation