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On the Influence of Machine Translation on Language Origin Obfuscation

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Computational Linguistics and Intelligent Text Processing (CICLing 2018)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13396))

Abstract

In the last decade, machine translation has become a popular means to deal with multilingual digital content. By providing higher quality translations, obfuscating the source language of a text becomes more attractive. In this paper, we analyze the ability to detect the source language from the translated output of two widely used commercial machine translation systems by utilizing machine-learning algorithms with basic textual features like n-grams. Evaluations show that the source language can be reconstructed with high accuracy for documents that contain a sufficient amount of translated text. In addition, we analyze how the document size influences the performance of the prediction, as well as how limiting the set of possible source languages improves the classification accuracy.

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Notes

  1. 1.

    https://www.blog.google/products/translate/found-translation-more-accurate-fluent-sentences-google-translate/.

  2. 2.

    https://blogs.msdn.microsoft.com/translation/2016/11/15/microsoft-translator-launching-neural-network-based-translations-for-all-its-speech-languages/.

  3. 3.

    https://translate.google.com.

  4. 4.

    https://www.microsoft.com/en-us/translator/.

  5. 5.

    The sampling on a random basis was required since the lcc dataset only contains random sentences, rather than cohesive documents.

  6. 6.

    e.g., https://blog.bufferapp.com/optimal-length-social-media.

  7. 7.

    http://scikit-learn.org/.

  8. 8.

    https://spacy.io/.

  9. 9.

     \(\hat{\,}\)_\(\texttt {`|~}\).

  10. 10.

    We rely on the list of stop words provided by scikit-learn.

  11. 11.

    http://scikit-learn.org/stable/modules/feature_extraction.html#tfidf-term-weighting.

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Authors and Affiliations

  1. Department of Computer Science, University of Innsbruck, Innsbruck, Austria

    Benjamin Murauer, Michael Tschuggnall & Günther Specht

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  1. Benjamin Murauer
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  2. Michael Tschuggnall
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  3. Günther Specht
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Correspondence to Benjamin Murauer .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Murauer, B., Tschuggnall, M., Specht, G. (2023). On the Influence of Machine Translation on Language Origin Obfuscation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13396. Springer, Cham. https://doi.org/10.1007/978-3-031-23793-5_26

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  • DOI: https://doi.org/10.1007/978-3-031-23793-5_26

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