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Proceedings of the 28th International Conference on Computational Linguistics
Deep neural models tremendously improved machine translation. In this context, we investigate whether distinguishing machine from human translations is still feasible. We trained and applied 18 classifiers under two settings: a monolingual task, in which the classifier only looks at the (French) translation; and a bilingual task, in which the source text (in English) is also taken into consideration. We report on extensive experiments involving 4 neural MT systems (Google Translate, DeepL, asdoi:10.18653/v1/2020.coling-main.576 fatcat:rv2m5hwpojbn7gelzm26zjirv4