  Translated texts are distinctively different from original ones, to the
extent that supervised text classification methods can distinguish between them
with high accuracy. These differences were proven useful for statistical
machine translation. However, it has been suggested that the accuracy of
translation detection deteriorates when the classifier is evaluated outside the
domain it was trained on. We show that this is indeed the case, in a variety of
evaluation scenarios. We then show that unsupervised classification is highly
accurate on this task. We suggest a method for determining the correct labels
of the clustering outcomes, and then use the labels for voting, improving the
accuracy even further. Moreover, we suggest a simple method for clustering in
the challenging case of mixed-domain datasets, in spite of the dominance of
domain-related features over translation-related ones. The result is an
effective, fully-unsupervised method for distinguishing between original and
translated texts that can be applied to new domains with reasonable accuracy.
