  This work distinguishes between translated and original text in the UN
protocol corpus. By modeling the problem as classification problem, we can
achieve up to 95% classification accuracy. We begin by deriving a parallel
corpus for different language-pairs annotated for translation direction, and
then classify the data by using various feature extraction methods. We compare
the different methods as well as the ability to distinguish between translated
and original texts in the different languages. The annotated corpus is publicly
available.
