@inproceedings{kunilovskaya-etal-2021-fiction,
title = "Fiction in {R}ussian Translation: A Translationese Study",
author = "Kunilovskaya, Maria and
Lapshinova-Koltunski, Ekaterina and
Mitkov, Ruslan",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.84/",
pages = "734--743",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Fiction in Russian Translation: A Translationese Study
%A Kunilovskaya, Maria
%A Lapshinova-Koltunski, Ekaterina
%A Mitkov, Ruslan
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F kunilovskaya-etal-2021-fiction
%X 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.
%U https://aclanthology.org/2021.ranlp-1.84/
%P 734-743
Markdown (Informal)
[Fiction in Russian Translation: A Translationese Study](https://aclanthology.org/2021.ranlp-1.84/) (Kunilovskaya et al., RANLP 2021)
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..