Quantifying the Translator Effect: Identifying authors and

machine translation tools in translated text

Aylin CaliskanComputer Science PhD Student

at Drexel University

Authorship Attribution

Identifying the author of an anonymous text Software JStylo Training corpus Lexical, character, syntactic, semantic features Machine learning tools

Brennan-Greenstadt Adversarial Stylometry Corpus

21st century writing Diverse topics 13 authors Native English authors Minimum of 5000 words per author Exclude obfuscation and imitation texts

Translator Effect

Original text: “This paper set out to explore what communities who seek greater local control over their lives face as the world begins the herculean task of reorganizing its energy systems.”

Google's translator: “In this paper, we explore the world to look for more local control of community life from what it is like to face a very difficult task to begin to rebuild their energy systems.”

Identifying translators

Authorship Attribution in Translated Text

Effective features in authorship attribution and translator attribution

Conclusion

Future Work

Different types of translators One way translations Arabic, Russian, Spanish, and Turkish corpus

Thanks for listening.

Any questions?Feel free to contact me: aylin.caliskan@drexel.edu