Translator attribution for Arabic using machine learning

Given a set of target language documents and their translators, the translator attribution task aims at identifying which translator translated which documents. The attribution and the identification of the translator’s style could contribute to fields including translation studies, digital humanities, and forensic linguistics. To conduct this investigation, firstly, we develop a new corpus containing the translations of world-famous books into Arabic. We then pre-process the books in our corpus which mainly involves cleaning irrelevant material, morphological segmentation analysis of words, and devocalization. After pre-processing the books, we propose to use 100 most frequent words and/or morphologically segmented function words as writing style markers of the translators (i.e. stylometric features) to differentiate between translations of different translators. After the completion of features extraction process, we applied several supervised and unsupervised machine-learning algorithms along with our novel cluster-to-author index to perform this task. We found that the translators are not invisible, and morphological analysis may not be more useful than just using the 100 most frequent words as features. The support vector machine linear kernel algorithm reported 99% classification accuracy. Similar findings were reported by the unsupervised machine-learning methods, namely, K-mean clustering and hierarchical clustering