Translated Texts under the Lens: from Machine Translation Detection to Source Language Identification

Machine Translation Systems are today used to break down linguistic barriers. People from different countries and languages can now interact with each other thanks to state-of-the-art translators from prominent software companies like Google and Microsoft. However, these tools are also used to expand the audience for phishing attacks, scam emails or to generate fake reviews to promote a product on different e-commerce platforms. In all these cases, detecting whether a text has been translated can be crucial information. In this work, we tackle the problem of the detection of translated texts from different angles. On top of addressing the classic task of machine translation detection, we investigate and find common patterns across different machine translation systems unrelated to the original text's source language. Then, we show that it is possible to identify the machine translation system used to generate a translated text with high performances (F1-score 88.5%) and that it is also possible to identify the source language of the original text. We perform our tasks over two datasets that we use to evaluate our models: Books, a new dataset we built from scratch based on excerpts of novels, and the well-known Europarl dataset, based on proceedings of the European Parliament.