Personalized Machine Translation: Preserving Original Author Traits
Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, Shuly Wintner
Abstract
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 domain-adaptation 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.- Anthology ID:
- E17-1101
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1074–1084
- Language:
- URL:
- https://aclanthology.org/E17-1101
- DOI:
- Cite (ACL):
- Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, and Shuly Wintner. 2017. Personalized Machine Translation: Preserving Original Author Traits. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1074–1084, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Personalized Machine Translation: Preserving Original Author Traits (Rabinovich et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/E17-1101.pdf