The language that we produce reflects our personality, and various personal and demographic characteristics can be detected in natural language texts. We focus on one particular personal trait of the author, gender, and study how it is manifested in original texts and in translations. We show that author's gender has a powerful, clear signal in originals texts, but this signal is obfuscated in human and machine translation. We then propose simple domainadaptation techniques that help retain the original gender traits in the translation, without harming the quality of the translation, thereby creating more personalized machine translation systems.
|Title of host publication||Long Papers - Continued|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||11|
|State||Published - 2017|
|Event||15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain|
Duration: 3 Apr 2017 → 7 Apr 2017
|Name||15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference|
|Conference||15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017|
|Period||3/04/17 → 7/04/17|
Bibliographical notePublisher Copyright:
© 2017 Association for Computational Linguistics.
ASJC Scopus subject areas
- Linguistics and Language
- Language and Linguistics