Translationese in R ussian Literary Texts (2021)

Abstract: The paper reports the results of a translationese study of literary texts based on translated and non-translated Russian. We aim to find out if translations deviate from non-translated literary texts, and if the established differences can be attributed to typological relations between source and target languages. We expect that literary translations from typologically distant languages should exhibit more translationese, and the fingerprints of individual source languages (and their families) are traceable in translations. We explore linguistic properties that distinguish non-translated Russian literature from translations into Russian. Our results show that non-translated fiction is different from translations to the degree that these two language varieties can be automatically classified. As expected, language typology is reflected in translations of literary texts. We identified features that point to linguistic specificity of Russian non-translated literature and to shining-through effects. Some of translationese features cut across all language pairs, while others are characteristic of literary translations from languages belonging to specific language families.

## introduction in this paper, we pursue a comparative analysis of literary texts that include texts originally authored in russian and russian translations from a variety of source languages (sls) using machine learning methods. we seek to establish if and how non-translated literature in russian differs from literary translations. more specifically, we test if literary translations from typologically distant languages (in relation to the target language @xcite ) exhibit more translationese, and hence, are easier to predict in a text classification scenario. there are only a few computational studies based on literary translations. intuitively, literary texts should defy generalisation as each author's style and each literary work is expected to manifest unique forms of artistic expression. besides, literary translation is viewed as an independent creative endeavour, which, under the dominant covert translation strategy, results in highly readable and fluent texts. logically, translations in this register might be less dependent on their source texts in terms of frequency of linguistic features. to the best of our knowledge, there were no large-scale investigations into russian translations of literary texts. with a view to analysing translationese in literary texts, we formulate two research questions. first, this study seeks to establish if nontranslated literary texts differ from literary translations, and if they do, which linguistic properties distinguish non-translated russian literature from translations into russian (rq1). second, this study has the objective to find out if the fingerprints of individual sls and their typological families can be traced in russian translations (rq2). it has been shown that translationese "dialects" generated by typologically similar languages tend to demonstrate more similarities than those coming from typologically distant sls (see section 2). to explore the impact of sls on the target text properties, we use a set of machine learning experiments automatically classifying translated and non-translated fiction in russian in binary and multiclass scenarios. this study reports the classification performance and identifies discriminative features of translated and non-translated literature. we conjecture that this investigation can yield empirical evidence of various translational strategies pursued in individual language pairs. all linguistic inconsistencies in relations between translations and non-translations for specific language pairs that we identify can be further tackled from the point of view of critical translation studies, which focuses on social and cultural practices in translation. ## related work translationese and literary texts translationese studies analyse linguistic features shared by translations that make them distinct from comparable non-translated texts @xcite . the deviations of translations from comparable originally-authored texts in the target language (referred to as 'non-translations' in this paper) are revealed through the analysis of translationese indicators, i.e. linguistic features that exhibit a different frequency distribution in translations as compared to non-translations. these features are used to automatically distinguish translations from non-translations @xcite @xcite @xcite . the choice of candidate translationese indicators is usually motivated by expectations of specific translational behaviours based on cross-linguistic contrastive properties of a given language pair or by (theoretically) known patterns in translations. over the decades of translationese research, several dozens of features were tested, with some being especially effective in a number of settings. features used for analysis of translationese are also widely used in register analysis, variational linguistics and stylometry. several studies have shown that combinations perform better than individual features in machine learning setting for a number of translationese-related tasks @xcite @xcite . however, it is also a fact that translationese indicators can be specific for language pairs and registers, and literary translation is an most corpus-based translationese studies of literary texts offer a single-feature analysis and do not employ machine learning (ml), e.g. passive constructions @xcite , that-complementiser (olohan, 2001), phrasal verbs @xcite , complex non-finite constructions, clause connectives and keywords @xcite . @xcite and @xcite are the only works known to us that use ml approach to the study of translationese in literary texts. the first study compared originally-written english literary texts with translations from french and german using character 5-grams. the second study reports the results of sl detection based on english literary translations from russian, german and french. their four-class classifier achieved the accuracy of 80% on a combined set of 50 features, including such surface features as frequencies of most-used part-of-speech (pos) bigrams, 15 top token unigrams (mostly functional words) and 19 document-level features such as sentenceand word-length and readability scores. the authors analysed some of the top-ranking features trying to link them to the sls. we propose a similar experiment: we run a multiclass classification on a set of features trying to identify best predictors. however, we rely on very different -more interpretable, language-specific and translationallymotivated -features and experiment on a twice bigger data from a wider range of sls. the mapping of individual features to known tendencies in translational behaviour was shown to be disputable, with one feature being associated with various tendencies @xcite , and we refrain from establishing the indicator-tendency link in the top-down manner. source language detection source language detection task utilises the fact that translations tend to retain enough traces of the sls involved (shiningthrough effect, @xcite for the benefits of linguistic typology and practical purposes of establishing translation direction in parallel corpora. for instance, @xcite showed that the signal from the source language is enough to restore the phylogenetic languages trees. the authors experimented on english translations of european parliamentary speeches from languages representing three families and showed that classification errors most frequently occurred between languages from the same family. sls with isomorphic structures tend to share more features in translations to a third language. that idea was exploited by @xcite who explored genetic, geographical and structural distances between languages and found that similarities between embeddings learnt from translations correlated best with the structural distance values obtained from vectorised treebanks of respective sls meaning that translations carried over structural properties of their sources, while geographic coordinates and genetic distances calculated from phylogenetic trees did not correlate with the properties of translations as well. dutta chowdhury et al. (2020) showed that the more isomorphism was detected between translations into english and non-translated english, the closer the source languages were to english. they learned delexicalised multi-view representations -embeddings based on pos, semantic tags, and synsets -and used isomorphism between embedding spaces. however, all these studies used parliamentary speeches, which belong to a more conventionalised text type than fiction. these texts, as well as their translations, are more homogeneous in style in comparison to literary texts used in the present paper. the translators are also more constrained in their linguistic choices by the requirements of official and documentary nature of texts. all the studies above cover english as the target language, whereas we focus on russian. a recent study by @xcite showed that translationese features not discriminative for english translations worked for a different language. the authors analysed chinese translations of journalistic texts from seven sls in a ml classification approach. this study stands out, because it defined chinese-specific translationese features, while most research in the field relied on easilyextractable language-independent features. they made observations on typological traces of the sls manifested in the tl-unusual frequencies of certain feature groups. sl-tl distance and the strength of the sl signal in translations is also key to solving the task of translation direction detection: @xcite found that the more distant were the source and the target languages, the higher the results. the authors achieved accuracy of 80-90%, even though they were challenged to perform classification at sentence-level. their feature set was designed to address the sparsity of data, inevitable if typical document-level features were used. they relied on alignment of phrases identified as 'minimal translation units' and represented with pos. the relation between predictability of translations and the divergence between the source and the target languages was also analysed by @xcite . the authors used entropy measures to check whether morphosyntactic entropies of original-language corpora are significantly different from those of corpora containing translations foot_0 . the results showed that translations from similar and distant languages were predictable 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 translation specificity. in translations from highly-divergent languages, however, translators tended to produce non-idiomatic translations, that were not recognised by models trained on the target language. ## conclusion we focused on the phenomenon of translationese in russian literature to find out that russian nontranslated literary texts are rarely confused with translations, and thus are very distinct. translated russian literary texts differ from non-translated ones to the degree that they can be automatically detected and the source language signal is strong enough to be traced in translations. as assumed, typologically close languages are more frequently confused with each other than more distant languages, which points to language typology being reflected also in translations of literary texts. the analysed features that distinguish translations and non-translations help to uncover specificity of russian non-translated literature. we were also able to detect some universal features of translationese in fiction, as well as language-specific and languagetypology-specific translation features. we plan to use these results as guidance in a more qualitative analysis of the features that behave differently in translated language, and of language pairs (esp. bulgarian and french) which were found diverting from the observed trend, in the framework of critical translation studies.