Fiction in Russian Translation: A Translationese Study
Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski, Ruslan Mitkov
Abstract
This paper presents a translationese study based on the parallel data from the Russian National Corpus (RNC). We explored differences between literary texts originally authored in Russian and fiction translated into Russian from 11 languages. The texts are represented with frequency-based features that capture structural and lexical properties of language. Binary classification results indicate that literary translations can be distinguished from non-translations with an accuracy ranging from 82 to 92% depending on the source language and feature set. Multiclass classification confirms that translations from distant languages are more distinct from non-translations than translations from languages that are typologically close to Russian. It also demonstrates that translations from same-family source languages share translationese properties. Structural features return more consistent results than features relying on external resources and capturing lexical properties of texts in both translationese detection and source language identification tasks.- Anthology ID:
- 2021.ranlp-1.84
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 734–743
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.84
- DOI:
- Cite (ACL):
- Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski, and Ruslan Mitkov. 2021. Fiction in Russian Translation: A Translationese Study. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 734–743, Held Online. INCOMA Ltd..
- Cite (Informal):
- Fiction in Russian Translation: A Translationese Study (Kunilovskaya et al., RANLP 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.ranlp-1.84.pdf