Unsupervised Identification of Translationese
This addresses the challenge of translation detection deteriorating outside training domains, offering an unsupervised method for linguists and NLP applications.
The paper tackled the problem of distinguishing original from translated texts, showing that unsupervised classification is highly accurate and can be improved with label assignment and voting, achieving reasonable accuracy across new domains.
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.