Paper: A Supervised Learning Approach Towards Profiling the Preservation of Authorial Style in Literary Translations

ACL ID C14-1037
Title A Supervised Learning Approach Towards Profiling the Preservation of Authorial Style in Literary Translations
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

Recently there has been growing interest in the application of approaches from the text classi- fication literature to fine-grained problems of textual stylometry. This paper seeks to answer a question which has concerned the translation studies community: how does a literary transla- tor?s style vary across their translations of different authors? This study focuses on the works of Constance Garnett, one of the most prolific English-language translators of Russian literature, and uses supervised learning approaches to analyse her translations of three well-known Rus- sian authors, Ivan Turgenev, Fyodor Dosteyevsky and Anton Chekhov. This analysis seeks to identify common linguistic patterns which hold for all of the translations from the same author. Based on the experimental results, it ...