Fiction in R ussian Translation: A Translationese Study (2021)

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.

## introduction this paper reports results of a translationese study based on literary texts translated from 11 source languages into russian and included into the russian national corpus foot_0 . translationese is understood as specific linguistic properties of translations distinguishing them from non-translated language. most existing investigations into the properties of translations have focused on text types other than fiction and, to the best of our knowledge, there were no large-scale investigations into translational russian, especially based on literary texts. the research applications of the parallel multilingual corpus of translations into russian are limited to constrastive studies such as @xcite and translation studies such as @xcite , and deal with a few individual items or constructions. we explore linguistic differences between fiction translated into russian and originally-authored literature in russian with a focus on the impact of the source language @xcite . the comparison between translated and nontranslated russian is based on an extended feature set capturing structural and abstract lexical parameters of texts, including collocational properties. this investigation mostly relies on the text classification approach and univariate statistical analyses following a methodology established in computational studies of translationese @xcite @xcite . translationese indicators that cut across all translated subcorpora (if any) or are specific to a particular language pair are identified through feature selection. it does not come as a surprise that researchers are wary of using fiction for translationese studies. fiction can be less homogeneous register because each literary work and author might display unique conceptual and linguistic properties reflecting a particular manner of artistic expression and idiolect. with literary translation established as a form of creative endeavour of its own standing and with the covert 'domesticating' translation paradigm accepted as a professional standard, little difference between translations and non-translations is to be expected in this register. this study is based on a carefully balanced and representative sample of literary texts from each language pair designed to reduce possible impact of author/translator idiolects on the results. we aim to establish if and how russian nontranslated literary texts differ from literary translations from a variety of sls (our first research question, rq1). following a recent trend in translationese studies @xcite @xcite , we are interested in exploring the link between the amount of translationese and the sl involved. translations from more distant languages were shown to return better classification results indicating greater differences from non-translations. can we corroborate this finding on our data and features (our second research question, rq2)? our results are relevant to typological and contrastive studies that use parallel data to draw conclusions about the properties of target language (tl) because we are highlighting that translations constitute a tl variety, significantly diverging from comparable non-translations. this study also contributes to a direction in translationese studies that seeks to establish links between sl-tl crosslinguistic distance and the amount of observed translationese as well as to the task of documentlevel sl detection. ## related work translations vs non-translations our work is related to studies showing that translated texts share linguistic features that make them distinct from non-translations @xcite . these features have been useful in automatic classification of translated and non-translated texts @xcite @xcite . some studies show that combinations of features perform better in machine learning settings for translationese-related tasks @xcite @xcite . this paper compares the effectiveness of two feature sets: morphosyntactic and lexical indicators. the former are count-based features extracted directly from automatically annotated research data. the latter are estimated using language models trained on a large, registercomparable tl corpus. they capture dissimilarity of translations with regard to the tl norm manifested in this additional tl resource. translationese in literary texts there are several corpus-based studies of literary translations. most of them explore single features: passive constructions @xcite , that-complementiser @xcite , non-finite constructions, phrasal verbs, connectives (ku-nilovskaya, 2017), keywords @xcite , etc. the only two works known to us that use machine learning to study translationese in literary texts include @xcite and @xcite . the first study explored the effectiveness of character 5-grams comparing originallywritten english literature with translations from french and german. the second study is more similar to the design proposed in this study. it reports the results of sl detection based on english literary translations from russian, german and french. a support vector machine (svm) 4-class classifier achieves the accuracy of 80% in the train-test scenario using a combined set of 50 features. the authors attempt the analysis of some of the topranking features, trying to link them to the sls. in our work, we also employ multiclass classification to identify best translationese predictors. however, we rely on very different features and experiment on a twice bigger data from a wider range of sls. tracing source languages source language detection is based on the assumption that translations tend to retain features of the sls (shining through effect, @xcite . @xcite showed that translations carried enough signal from sls to restore the phylogenetic language tree. the authors experimented on english translations of the european parliamentary speeches in 17 sls representing three language families. they noticed that misclassified instances were frequently assigned to genetically related languages. translations from sls with isomorphic structures displayed a tendency to share more translationese features. another study by @xcite used three levels of syntactic abstraction to explore genetic, geographical and structural distances between sls. in their results, structural similarities were a better predictor of similarities between languages than genetic ones. dutta @xcite used isomorphism between embedding spaces, hypothesising that the more isomorphism was detected between translations into english and non-translated english, the closer the source and target languages. they learned delexicalised multiview representations -embeddings based on partsof-speech (pos) tags, lexical semantic tags, and conceptual-semantic tags from wordnet. however, these studies are based on europarl, a corpus of parliamentary speeches, which might be more homogeneous in style, more conventionalised as a text type and in terms of translation strategies than literary texts focused in this paper. the relation between predictability of translated texts and the divergence between the tl and the sl in terms of morpho-syntax were analysed in a recent study by @xcite . the authors used parallel universal dependencies to analyse 1000 sentences from the news domain and wikipedia translated from english into eight languages. the results showed that translations from similar and distant languages were both predictable, but in different ways: structurally-similar sls favoured the use of a narrower range of syntactic patterns limited to those shared by two languages, which constituted one type of translational specificity. in translations from highly-divergent languages, however, translators tended to produce non-idiomatic renditions, that were not recognised by models trained on the tl. @xcite also used the sl signal to detect translation direction. they found that the more distant were the source and the target languages, the higher the sl-detection results. ## results and discussion to find out whether our features capture any differences between translations and non-translations in sl subcorpora and whether the scale of these differences is traceable to the typological group of the sl, we ran 11 binary classifications and a multiclass classification. in all experiments we used support vector machine (svm) algorithm with the default scikit-learn settings (@xmath0.0, @xmath1, @xmath2, @xmath3. for the five subcorpora where we have fewer observations than in the reference sample, we used @xmath4. f1-score (f1) results of the binary classifications for each translational subcorpus (represented by sl) against non-translations. the sls are presented in two groups: typologically distant and close languages (in relation to russian). we also group the scores according to the feature sets and combinations: all 68 features, 45 ud features (structural features), 11 n-gram and 12 collgram (both abstract lexical features). the last two columns show the results expected by chance. this simple baseline was implemented as a random classifier which predicts classes with respect to the distribution of instances across classes. we report the scores for the 10-fold cross-validation scenario. rq1 overall, we are able to detect differences between translated and non-translated fiction in russian. combined features and each feature set individually performed better than the chance-level baseline, except collocational features for translated spanish, which were on par with the dummy classifier. remarkably, structural features (45 ud in to explore the impact of the individual features on the classification outcome, we employed recursive feature elimination (rfe) algorithm based on support vector regressor (svr), which selected a unique combination of n features. rfe selects features that return the best classification results. there were only two features shared in rfe-based selections by all classifiers, if @xmath16: the ratio of high frequency trigrams and the frequency of modal predicates (defined as the the cumulative frequency of lemma мочь, lemma следовать with a dependent infinitive, three modal adverbs (можно, нельзя, надо) and 11 adjectives in the short form from a modal predicative list (e.g. должен, способный, возможный)). however, the frequency analysis for these features shows no significant differences in their values in many test subcorpora. even if the difference was significant at @xmath17.05, the effect size did not exceed cohen's d of 0.2. ## conclusion we analysed translationese in literary texts, exploring differences between fiction originally authored in russian and fiction translated into russian from 11 languages. we found out that overall, we can automatically predict translations in this register with the accuracy well above the chance level. besides, we compared performance of classifiers for translations from various sls. we expected that translations from more distant sls would return higher results in a series of binary classifications and would be easier to recognise in a multiclass setting than translations from typologically closer languages. this was confirmed for the subset of structural features, but not for lexical features, which returned mixed results. possible explanations for the opposite tendencies observed in the data when using lexical features could be deficiency of the reference model and variation in literature style or literary translation tradition. at the same time, we also understand the limitations of our data selection and research design. for example, experiments demonstrate that average sentence length might have been a better indicator of genre comparability than the length of a literary piece. the reference model for collocational features should probably be trained on a larger corpus to ensure greater coverage of lexical items. we leave detailed statistical analysis of the best-performing features for each language group and case studies based on parallel concordances for future work.