Academic journal article Translation & Interpreting

Learner vs. Professional Translations into Russian: Lexical Profiles

Academic journal article Translation & Interpreting

Learner vs. Professional Translations into Russian: Lexical Profiles

Article excerpt

1. Aims, motivation and key concepts

There is hardly a textbook in the translation literature that does not discuss translation quality. Many of them focus perceived translational problems in a given language pair based on the cross-linguistic differences and suggest that novice translators perform worse than professionals. The prescriptive and evaluative bias in translator education can be to some extent offset by the descriptive approach. The latter approach posits that all translations, regardless of the translator's professional standing, appear to differ from non-translations (Chesterman, 2004) in their linguistic characteristics even if they are not easily detected by the naked eye. In the study reported on in this paper, we employed corpus linguistics methods to understand and measure differences in students' output as distinct from published professional translations. In doing so, we consider whether these two sociolinguistic varieties can be grouped together as representing different degrees of translationese (Gellerstam, 1986) in comparison with non-translations.

Throughout this research translationese is used as a general non-evaluative term to refer to the quantitative linguistic features of translations that set them apart from non-translations in the same language. This interpretation is traditional in European corpus-based translation studies and is in line with with the notion of translated discourse offered by Garbovskyi (2012) in Russia. Though the notion of translationese is not alien to Russian translation studies, Russian translated discourse has not received much attention from corpus linguists, with the thesis by Krasnopeeva (2015) being an exception. It remains largely undescribed, and therefore, attractive.

Professional translation is another key concept here. For the purposes of this research professional translations are translations published by an official information agency under the translator's name or endorsed by the agency's editorial board. We assume that the translators are employed by such agencies. Professional translations are introduced into the research design as a benchmark of translational quality. As such, they can be distinguished from non-translations. Learner translators are also defined socially rather than in terms of linguistic or professional competence: they are full-time students enrolled in Masters in Linguistics university programs with a translation or translation studies focus.

Professional translations into Russian have been the subject of corpus-based scrutiny in the recent dissertation by Krasnopeeva (2015). She found statistically significant lexical dissimilarities between originally-authored Russian belles-lettres prose and its comparable translated counterpart. Her study is based on a corpus of English-to-Russian translations and translations into Russian from a variety of other languages. The results confirm translational tendencies of explicitation, simplification, convergence and normalization. Though this work focuses on fiction, a genre with a different sociopragmatic function, it is important for the current research as one of the few comprehensive and methodologically well-balanced studies of Russian translated discourse.

In this paper we compare translations with non-translations in Russian and contrast them against their source texts according to a number of textual parameters. The select indicators of linguistic difference between the corpora include statistics on sentence length and frequencies of morphological forms of word classes, as well as such characteristics of texts as lexical variety and lexical density. To this end we designed a system of genre-comparable corpora formed by collections of learner and professional translations into Russian with their respective source texts in English and a reference corpus of non-translated Russian from The Russian National Corpus (RNC). All the texts in our dataset are newspaper articles from a variety of topical domains published in electronic mass media over the last decade (naturally, except learner translations). …

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