Lexicogrammatic translationese across two targets and competence levels

This research employs genre-comparable data from a number of parallel and comparable corpora to explore the specificity of translations from English into German and Russian produced by students and professional translators. We introduce an elaborate set of human-interpretable lexicogrammatic translationese indicators and calculate the amount of translationese manifested in the data for each target language and translation variety. By placing translations into the same feature space as their sources and the genre-comparable non-translated reference texts in the target language, we observe two separate translationese effects: a shift of translations into the gap between the two languages and a shift away from either language. These trends are linked to the features that contribute to each of the effects. Finally, we compare the translation varieties and find out that the professionalism levels seem to have some correlation with the amount and types of translationese detected, while each language pair demonstrates a specific socio-linguistically determined combination of the translationese effects.

[1]  Shuly Wintner,et al.  Found in Translation: Reconstructing Phylogenetic Language Trees from Translations , 2017, ACL.

[2]  Serge Sharoff,et al.  Applying Multi-Dimensional Analysis to a Russian Webcorpus: Searching for Evidence of Genres , 2015, BSNLP@RANLP.

[3]  Milan Straka,et al.  Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe , 2017, CoNLL.

[4]  Josef van Genabith,et al.  Information Density and Quality Estimation Features as Translationese Indicators for Human Translation Classification , 2016, NAACL.

[5]  Mona Baker.  'Corpora in Translation Studies: An Overview and Some Suggestions for Future Research' , 1995 .

[6]  Serge Sharoff,et al.  Towards Functionally Similar Corpus Resources for Translation , 2019, RANLP.

[7]  Lisette García Moya,et al.  Translation universals : do they exist ? A corpus-based and NLP approach to convergence , 2008 .

[8]  Marius Popescu,et al.  Studying Translationese at the Character Level , 2011, RANLP.

[9]  Erich Steiner,et al.  Cross-Linguistic Corpora for the Study of Translations: Insights from the Language Pair English-German , 2012 .

[10]  Shuly Wintner,et al.  On the features of translationese , 2015, Digit. Scholarsh. Humanit..

[11]  Stella Neumann,et al.  Contrastive Register Variation: A Quantitative Approach to the Comparison of English and German , 2013, Modern Language Review.

[12]  Ekaterina Lapshinova-Koltunski.  VARTRA: A Comparable Corpus for Analysis of Translation Variation , 2013, BUCC@ACL.

[13]  Douglas Biber,et al.  Variation across speech and writing: Methodology , 1988 .

[14]  Silvia Bernardini,et al.  Corpora for translator education and translation practice , 2008 .

[15]  Silvia Bernardini,et al.  A New Approach to the Study of Translationese: Machine-learning the Difference between Original and Translated Text , 2005, Lit. Linguistic Comput..

[16]  Haitao Liu,et al.  Mean Hierarchical Distance Augmenting Mean Dependency Distance , 2015, DepLing.

[17]  飯島 周.  「会話の文法」に関する一考察 : Longman Grammar of Spoken and Written Englishの場合 , 1999 .

[18]  Stefan Evert,et al.  The impact of translation direction on characteristics of translated texts. A multivariate analysis for English and German , 2017 .

[19]  Maria Kunilovskaya,et al.  Universal Dependencies-based syntactic features in detecting human translation varieties , 2018, TLT.

[20]  Elke Teich,et al.  Cross-linguistic variation in system and text : a methodology for the investigation of translations and comparable texts , 2003 .

[21]  Куниловская Мария Анатольевна.  Классификация переводческих ошибок для создания разметки в учебном параллельном корпусе Russian Learner translator Corpus , 2013 .

[22]  Martin Gellerstam,et al.  Translationese in Swedish novels translated from English , 1986 .