Identification of translationese: A machine learning approach

Abstract

This paper presents a machine learning approach to the study of translationese. The goal is to train a computer system to distinguish between translated and non-translated text, in order to determine the characteristic features that influence the classifiers. Several algorithms reach up to 97.62% success rate on a technical dataset. Moreover, the SVMclassifier consistently reports a statistically significant improved accuracy when the learning system benefits from the addition of simpli-fication features to the basic translational classifier system. Therefore, these findings may be considered an argument for the existence of the Simplification Universal. © Springer-Verlag 2010.