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Translationese and Register Variation
in English-To-Russian Professional
Translation
Maria Kunilovskaya and Gloria Corpas Pastor
Abstract This study explores the impact of register on the properties of transla-
tions. We compare sources, translations and non-translated reference texts to
describe the linguistic specificity of translations common and unique between four
registers. Our approach includes bottom-up identification of translationese effects
that can be used to define translations in relation to contrastive properties of each
register. The analysis is based on an extended set of features that reflect morpho-
logical, syntactic and text-level characteristics of translations. We also experiment
with lexis-based features from n-gram language models estimated on large bodies
of originally- authored texts from the included registers. Our parallel corpora are
built from published English-to-Russian professional translations of general domain
mass-media texts, popular-scientific books, fiction and analytical texts on political
and economic news. The number of observations and the data sizes for parallel and
reference components are comparable within each register and range from 166
(fiction) to 525 (media) text pairs; from 300,000 to 1 million tokens.
Methodologically, the research relies on a series of supervised and unsupervised
machine learning techniques, including those that facilitate visual data exploration.
We learn a number of text classification models and study their performance to
assess our hypotheses. Further on, we analyse the usefulness of the features for
these classifications to detect the best translationese indicators in each register. The
multivariate analysis via text classification is complemented by univariate statistical
analysis which helps to explain the observed deviation of translated registers
through a number of translationese effects and detect the features that contribute to
them. Our results demonstrate that each register generates a unique form of
translationese that can be only partially explained by cross-linguistic factors.
Translated registers differ in the amount and type of prevalent translationese. The
M. Kunilovskaya (&)G. Corpas Pastor
Research Group in Computational Linguistics, University of Wolverhampton,
Wolverhampton, UK
e-mail: maria.kunilovskaya@wlv.ac.uk
G. Corpas Pastor
University of Malaga, Malaga, Spain
©The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
V. X. Wang et al. (eds.), New Perspectives on Corpus Translation Studies,
New Frontiers in Translation Studies, https://doi.org/10.1007/978-981-16-4918-9_6
133
same translationese tendencies in different registers are manifested through different
features. In particular, the notorious shining-through effect is more noticeable in
general media texts and news commentary and is less prominent in fiction.
Keywords Parallel corpora Register variation Translationese trends
Translationese indicators Machine learning
1 Motivation and Aim
In this chapter we explore and compare translationese effects across several reg-
isters in English-to-Russian translation. This research builds on the long-established
assumption that the intralinguistic variation between registers can be greater than
the cross-linguistic differences between the same registers, famously demonstrated
by Biber (1999). We also assume that the cross-linguistic differences are one of the
major factors that shape the linguistic make-up of translations. The configuration of
differences and similarities between the source language (SL) and the target lan-
guage (TL) creates a unique language gap in each register and underlies the
shining-through effect (Teich 2003) or interference, i.e. the tendency of translated
texts to follow the SL patterns rather than conform to the regularities of the TL.
Based on these assumptions, we are interested in establishing how the
cross-linguistic distance between registers plays out with respect to the properties of
translated texts in these registers.
It is especially interesting because the features used in this research to distin-
guish translations from the originally-authored texts in the target language (also
referred to as non-translations or reference texts) are partly inspired by the varia-
tional linguistics studies that compare registers (Biber 1988; Katinskaya and Sharoff
2015; Neumann 2013; Nini 2015).
Besides variational studies, our feature selection and engineering process were
guided by the previous translationese studies and evidence from the empirical
translation studies, especially those that relied on interpretable (rather than surface)
linguistic features to describe the typical deviations from TL norm observed in
translations. Briefly, we use two feature sets: (i) frequencies of a number of mor-
phosyntactic categories extracted from Universal Dependencies (UD) annotations
and (ii) lexical frequency features that reflect the differences in the distribution of
n-grams in translated and non-translated language (a detailed description of features
is offered in Sect. 3.1; the description of the morphosyntactic features is offered in
Appendix).
Typical translationese features for English-to-Russian translation include the
overuse of relative clauses, copula verbs, modal predicates, analytical passives,
generic nouns and all types of pronouns as shown below. Probably, none of the
translation in the examples can be considered ungrammatical in Russian, but there
is a Master Yoda-style foreign sound to them. Note that the back translations may
134 M. Kunilovskaya and G. Corpas Pastor
come across as perfectly acceptable sentences, because the translations are very
literal in the first place. All examples are real-life student translations from Russian
Learner Translator corpus (Kutuzov and Kunilovskaya 2014).
1
(1) Necklaces, at first as pectorals that covered the whole chest, evolved from the
prehistoric pendants. Oжepeльe―пepвoe нaгpyднoe yкpaшeниe, кoтopoe
зaнимaлoмecтoнaвceйгpyди,кoтopoe cтaлoocнoвoй для пoдвecoк
[Necklace―first chest decoration, which covered the whole chest, which
became the basis for pendants].
(2) …there are many self-employed people who manage to get money from others
by means of falsely pretending to provide them with some benefit or service…
Бoлee тoгo, ecть мнoгoлюдeй,paбoтaющиxнaceбя,кoтopыeпoлyчaют
дeньги oбмaнным пyтeм[Moreover, many people are, working for them-
selves, who get the money in a deceitful way].
(3) …differences in self-efficacy may simply mean that some teachers struggle to
identify solutions to problems beyond their circle of control. …paзницaв
caмooцeнкeмoжeтoзнaчaть лишь тo, чтoнeкoтopыeyчитeля
иcпытывaют cлoжнocти в нaxoждeнии peшeний зaдaчзaпpeдeлaми тoгo,
чeмoни мoгyтyпpaвлять […difference in self-evaluation can mean only
that some teachers run into difficulties in finding solutions to tasks beyond the
scope of that what they can control].
(4) It was difficult and exhausting to see. Этoбылoтяжeлoиyтoмляющe
пытaтьcя видeть.[It was hard and exhausting to try to see].
These examples demonstrate a number of translation solutions that explain the
increase in the frequency of TL items that are less frequent in non-translated TL
than their literal counterparts in the SL. In example (2) the generic noun ‘people’is
rendered with a less frequent literal ‘люди’, instead of using a structure with zero
subject or other more acceptable ways of expressing unspecified subjects. English
and Russian have contrastive ways of expressing subjective modality: modal verbs
are a less common choice in non-translated Russian, which prefers parenthetical
means of expressing modality. The translation solution in (3) carries over the
typical English modal predicate. Example (4) has the notorious literal renderings of
the structures with the introductory it, which contributes to the boost of pronouns
and copula verbs in translated Russian. Besides, such renditions have a strange
word order, which usually interferes with the smooth flow of information in the
text. Another source of surplus function words, including pronouns is the tendency
to unpack the information from various concise English structures using strings of
relative clauses, instead of repackaging the information in a more natural way (see
(1) and (3)). Finally, example (2) demonstrates the tendency towards the explicit
use of copula verbs in contexts, where a zero copula is typical in Russian.
The overarching goal of this research is to reveal and describe the register-related
specificity of English-to-Russian translations in four registers.
1
https://www.rus-ltc.org/search.
Translationese and Register Variation …135
To achieve this goal, we complete several steps and answer the following
research questions:
1. How clear are the register distinctions between the translated registers compared
to non-translations for the two feature sets tested, provided that the suggested
features reliably distinguish registers in originally-authored Russian? If the
register distinctions are diluted in translations, the standardisation hypothesis
stands.
2. Do registers share translationese indicators, i.e. are there translationese indica-
tors that cut across all registers, provided that we are able to distinguish between
translations and non-translations using our features?
3. What are the most important translationese indicators and most prominent
translationese trends based on the results of multivariate and univariate analyses
in each register?
4. Do the top translationese indicators intersect with the major cross-linguistic
differences between the same registers in English and Russian to demonstrate
that interference is the most important translationese effect?
These research questions are relevant to the development of the translationese
theories and methodologies. The robustness of translationese indicators across
registers has to be considered while building translationese detection applications.
The register-induced specificity of translations has to be taken into consideration in
any translation quality estimation systems based on translationese features.
In what follows, we discuss the theoretical implications of the previous trans-
lationese and variational linguistics studies for the current research and define our
key concepts (Sect. 2). Section 3describes our research data and the linguistic
resources used for language modelling; it also has the description of our methods
and experimental setup, starting with the feature sets. The results as per the research
questions are presented and commented in Sect. 4, which is followed by their
interpretation in Sect. 5. Section 6summarises the research and outlines future
work.
2 Theoretical Background
2.1 Key Concepts and Approaches
The theoretical underpinnings for this research come from translationese studies, a
research direction that investigates the peculiarities of translated texts that distin-
guish them from non-translations. This research field is related to the tasks of
testing translationese universals, translationese detection, translation direction
detections (including SL identification both for human and machine translation
(MT)) as well as more recent studies of translationese variation along a number of
dimensions such as translation competence, quality, direction, method, etc. In our
136 M. Kunilovskaya and G. Corpas Pastor
necessarily sketchy discussion of the developments in this well-established research
area below, we highlight the aspects that are most relevant for the current project.
What is ‘translationese’.The foundations of this type of studies were laid by
Gellerstam (1986), to whom they attribute the introduction of the term ‘transla-
tionese’. Gellerstam has demonstrated that there were significant statistical differ-
ences in the frequencies of loan words and colloquialisms, among other lexical
features, between translated and non-translated Swedish texts. Originally, the term
was used to denote statistical deviations of the translated language from the
expected target language norm manifested in a reference corpus. Diana Santos
(1995) extended the lexical translationese findings to include morphological phe-
nomena such as diverging frequencies of tense and aspect forms in English and
Portuguese. Her research was based on a small bidirectional parallel corpus, which
provided enough occurrences of the targeted grammatical items for manual anal-
ysis. Importantly, her research design gave access to the source text and helped to
link the unusual frequencies of grammatical items to the influence of the source
text. We will highlight that her understanding of translationese was limited to ‘the
influence of properties of the source language in a translated text in a target lan-
guage’(Santos 1995: 61). Her work is relevant for this research because it explicitly
mentions the impact of the distance between the languages on the properties of
translations. In particular, the author hypothesises that the closer the languages, the
more probability of translationese due to the ease of levelling-out the differences
between them.
The term translationese is sometimes used metonymically to denote any trans-
lated material (see, Nikolaev et al. 2020; Stymne 2017, for example) or to refer to
the specificity of translations induced by the SL in opposition to SL/
TL-independent properties of translations known as translation universals (see
Rabadán et al. 2009; Santos 1995). For the purposes of this project, translationese is
defined as a property of being a translation, based on the statistical differences in
frequencies of language items between translations and non-translations in the TL
regardless of their hypothesised cause, which mark translations as its own lan-
guage variety.
Main translationese effects: Shining-through and independent transla-
tionese. Important developments in the descriptive approach to translations are
associated with Gideon Toury’slaws of translation (1995) and Mona Baker’s
translation universals hypotheses (1993). To put it briefly, the former generalised
the observations on the properties of translations as two major laws: the law of
increasing standardisation, and the law of interference from the source text. Mona
Baker’s theory suggested that there are universal tendencies in translation that are
independent of the source and target languages. Baker’s famous definition of the
universal features of translation runs as follows: ‘features which typically occur in
translated texts rather than original utterances and which are not the result of
interference from specific linguistic systems’(Baker 1993: 243). Her initial set of
hypothesised universals (among the most-tested items) included explicitation, i.e.
the tendency to spell things out rather than leave them implicit; simplification, i.e.
the tendency to disambiguate and to avoid any risks of misunderstanding by making
Translationese and Register Variation …137
texts simpler lexically and structurally; conventionalisation (also known as stan-
dardisation or levelling-out), i.e. the tendency for translations to exhibit relatively
higher level of homogeneity than their sources; normalisation, i.e. the tendency to
exaggerate features of the TL and to conform to its typical patterns.
The subsequent empiric research into translation universals did not corroborate
the initial ‘universal’claims for the proposed hypotheses. The results on a variety of
translated domains, registers, language pairs and translation varieties were mixed
and contradictory. To give some examples, Corpas Pastor et al. (2008) confirmed
simplification for some features associated with this trend, but not for the others.
Kruger and van Rooy reported limited support for the ‘more explicit, more con-
servative, and simplified language use in the translation corpus’(Kruger and van
Rooy 2010:26).
This is not surprising for three major reasons: (1) the mapping of particular
features into descriptive translationese trends can be a matter of debate (as stated in
Zanettin 2013: 25); (2) there can be differences in the extraction procedures;
(3) translations from different SLs and in different registers produce diverging
translationese patterns. To demonstrate some of these factors consider the findings
about connectives (also referred to as discourse markers, cohesive markers or
conjunctions). Corpas Pastor et al. (2008) expected fewer discourse markers in
translations of medical and technical texts from English into Spanish as a sign of
simplification, and indeed found that ‘non-translated texts use discourse markers
significantly more often’in two out of three corpus pairs (Corpas Pastor et al. 2008:
24). At the same time, Koppel and Ordan (2011), while testing on English trans-
lations of addresses given in the European Parliament (Europarl) in five other
languages, reported that discourse markers were significantly more frequent in
translations than in the originally-authored English texts. They were inclined to
interpret it as an indication of explicitation. Generally, the increase in the fre-
quencies of discourse markers in translated language and higher cohesiveness of
translations is a relatively well-explored translationese phenomenon. However, its
interpretation as a manifestation of explicitation, normalisation or SL interference
varies across language pairs and text categories or is unclear in some experimental
setups (Castagnoli 2009; Kunilovskaya 2017; Olohan 2001). It is especially con-
fusing if connectives are treated individually rather than cumulatively. In Jiang and
Tao (2017) the frequencies of individual discourse markers were traced to the
corresponding SL items to demonstrate that they contribute to several translation
universals. Similarly, Becher insisted that ‘every explicitating and implicitating
shift has a distinct cause’and needs to be treated on a case-to-case basis (Becher
2011: 215).
In this research we refrain from assigning individual features (indicators) to the
trends such as simplification and explicitation a priori. Instead, we follow a
bottom-up approach and identify the indicators of some translationese effects based
on the similarity of their frequency pattern in the source texts (ST), target texts
(TT) and reference texts (see Sect. 3.3 for the categorisation of features as con-
tributing to different translationese effects).
138 M. Kunilovskaya and G. Corpas Pastor
The two interpretations of the nature of translations given by Toury and by
Baker are complementary and can be seen to represent two major types of trans-
lationese. To avoid unnecessary associations with the foreign language acquisition
terminology, we would use Elke Teich’s term shining-through to refer to the cases
where the cross-linguistically diverging frequencies of the features are adapted in
translations to the SL values, giving rise to significant distinctions between trans-
lations and non-translations (Teich 2003). This is the ‘interference’type of trans-
lationese, which is considered the major factor in shaping the properties of
translations (see evidence in Evert and Neumann 2017; Volansky et al. 2015, for
example). The features of translations that significantly deviate from both SL and
TL, where there are no cross-linguistic differences between non-translations
(English source texts and originally-authored Russian texts in our setup), should
be considered cases of true language-pair-independent translationese in line with
Baker’s ideas. Some features that spot language contrast can be fully adapted to the
TL norm (adaptation) or even exaggerate the TL properties (over-normalisation or
russification in our setup).
Methodological paradigms in translationese studies (features, data and
analytical approaches). Over the last few decades, translationese studies as an area
of research within translation studies has seen significant developments in the
research methods. The earlier investigations were often based on manual extraction
of a few features from limited corpus data (sometimes lacking the parallel com-
ponent) and relied on univariate statistic analysis (Becher 2011; Castagnoli et al.
2011; Nakamura 2007; Puurtinen 2003; Santos 1995). The more recent projects are
computationally intensive and involve massive parallel and comparable corpus
resources in several language pairs and complex research designs with extensive
and elaborate feature sets and methods (see, for example, Dipper, Seiss, and
Zinsmeister (2012) who describe the typical corpus resources setup in translationese
studies and Evert and Neumann (2017) for the multivariate analysis and feature
engineering methodology).
Amachine learning (ML) turn in the translationese research began with the
ground-breaking work by Baroni and Bernardini (2006) who convincingly
demonstrated that translations of geopolitical texts into Italian are inherently dif-
ferent from the comparable non-translations by employing a Support Vector
Machines (SVM) algorithm to classify them. They experimented with various types
of n-grams to represent texts and discovered that bigrams performed best. An
important message from their experiments was that a ML algorithm was able to
reliably pick the difference between translations and non-translations even when the
human subjects (professional translators) were unable to do so as effectively. It
brought about a new strand of research known as translationese detection. ML
algorithms were used to test the hypothesis about various translationese properties.
A good example of this methodology in action is Koppel and Ordan (2011), who
reported a series of ML experiments on the Europarl corpus and confirmed that
source language plays a crucial role in the make-up of a translated text. They used
frequencies of 300 function words as features (which excludes any cultural or topic
differences between the corpora). Probably, the most impressive results were
Translationese and Register Variation …139
reported by Popescu (2011) who reported 99.53% cross-validation accuracy in the
task of detecting translations on character string features for an SVM classifier
trained on literary translations from French and German into English. However,
when they tested a model trained on out-of-French translations on out-of-German
translations they received the results at the chance level―an indication that char-
acter n-grams capture uninteresting SL-related cues such as proper names. Filtering
out those items led to the realistically moderate results of 77.08% in the experiment
where they trained on translations from French and books by British authors for
reference and testing on translations from German and American fiction for
non-translated reference.
In Ilisei et al (2010), a supervised learning approach was employed to identify
the most informative features that characterised translations compared to
non-translated texts. The learning system was trained on two domains, medical and
technical. The novelty of their approach consisted of its language-independent data
representation. On the categorisation task, the algorithms achieved an accuracy of
87.16% on a test set and reached up to 97.62% for separate test datasets from the
technical domain. The removal of the features, linked by the authors to simplifi-
cation, from the machine learning process led to decreased accuracy of the clas-
sifiers. Therefore, the retrieved results were interpreted as an argument for the
existence of the simplification universal.
The book by Gloria Corpas presents the results of several NLP experiments to
study translation universals and translationese features. Corpas focuses on three
universals: simplification, convergence and transfer (shining-through). Vectors of
lexical and syntactic features are used to test various corpora of English and
Spanish: (a) a large corpus of Peninsular Spanish (reference corpus of 50 million
words), and various comparable corpora: (a) corpus of translation of medical texts
by professionals and semi-professionals (from English into Spanish); (b) corpus of
non-translated medical texts in Spanish; c) corpus of non-translated medical texts in
English, (d) corpus of translation of technical texts by professionals (from English
into Spanish); and (d) corpus of non-translated technical texts in Spanish. The main
findings support (1) the inexistence of simplification of translated text into Spanish
(for most features) (non-translated Spanish texts are even more simple).
(2) Convergence (translated texts are more homogeneous among themselves) can
be observed only for syntactic features. (3) Transfer can only be observed partially:
there is some positive transfer (translated texts show more lexical cognates), but no
negative transfer (translated texts show more zero pronouns). Syntactic interference
(shining-through) is observed for all translated texts (Corpas Pastor 2008).
After the initial sweeping success of ML approaches to detecting translations on
surface and linguistically uninterpretable features, there appeared a research strand
that aimed to combine the ML computational power with the corpus-linguistic
interest in translationese properties. These efforts can be exemplified by Volansky,
Ordan, and Wintner (2015) research, which tested the usefulness of a dozen of
linguistically informed features, theoretically attributed to the main translation
tendencies (simplification, interference, normalisation and explicitation). In effect,
they used ML methodology to perform univariate analysis (they compare the
140 M. Kunilovskaya and G. Corpas Pastor
accuracy of a binary translationese classification on each feature) to reveal the
features prominence in the identification of translations. Their findings make a
strong argument for interference as the major tendency in translation and, con-
currently, for language-pair-related nature of translationese in general. The authors
also make rigorous claims about the importance of a parallel data,
content-independent features and genre-related nature of translationese trends.
The use of automatic text classification as a validation methodology combined
with unsupervised and mildly supervised machine learning techniques (namely,
Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)) was
promoted in Evert and Neumann (2017) for revealing the latent distinctions between
text types (languages, registers, translations-non-translations) and exploring the sets
of features that load on the respective discriminants. Unlike the previous research, the
authors advocated the use of the multivariate techniques claiming that translationese
is a systematic property of a text, not dissimilar to register specificity, and can hardly
be conveyed by a single feature, but rather a combination of them (cf. multidimen-
sional approach to register studies introduced by Biber (1988) and similar approach to
translation in (Prieels et al. 2015)). An important methodological claim that the
authors make is about the resources necessary for translationese studies. They assert
that ‘it is methodologically impossible to determine differences between translated
and non-translated texts without comparing the realisation of a feature in the
matching source text’(Evert and Neumann 2017: 49). It is interesting to note that
despite their study is based on a balanced corpus involving five registers, the register
variation was treated as a confounding factor that shapes translationese; any
register-related interpretations were left for future work.
Evidence for language pair specificity of translationese. While developing
effective language-independent applications to detect translations can be an inter-
esting engineering task, there is ample evidence that translationese features and
effects are indeed language pair and translation direction specific. In fact, the
symmetric additions and omissions of items in both translation directions between
two languages (demonstrated by Becher (2011), for example) are indicative of the
impact of the contrastive properties of the language pair on the translators’choices.
Reduced accuracy of the translationese classification, when a model trained on
translations from SL1 is tested on translations from SL2, supports the same con-
clusion (Koppel and Ordan 2011; Popescu 2011). It is common to interpret the
linguistic make-up of translations as a complex interplay of the two major forces:
the SL shining-through pull and the TL normalisation pull (see, for example,
Hansen-Schirra (2011)).
To sum up, the previous translationese research has established that translations
are systematically and inherently different from the originally-authored texts due to
the specificity of the underlying communicative situation and cognitive processes. It
has been shown that the property of ‘being a translation’is largely determined by
the SL and the register conventions. The intuitive association between some fre-
quency features and translationese universals proved difficult to be confirmed by
empirical evidence due to the lack of objective link between the trend and its
operationalisation. However, bottom-up exploratory approaches based on ML
Translationese and Register Variation …141
methods enable to reveal translationese indicators and the unique ways in which
they coalesce into patterns in each register of a given translation direction.
In general, the relevance of translationese studies is supported by the renewed
interest to the impact the human translated training data exerts on the quality of
machine translation (Aharoni et al. 2014; Goutte et al. 2009; Graham et al. 2019;
Popovic 2020; Stymne 2017; Zhang and Toral 2019). One of the earlier investi-
gations into this issue by Lembersky, Ordan, and Wintner (2012) demonstrated that
the BLEU score can be improved if the language models are trained on the
translated texts and not the texts originally written in the TL.
The current project is based on balanced data for four registers, each represented
by a combination of (1) a document- and sentence-aligned parallel corpus of pro-
fessional published translations for English-to-Russian language pair and (2) a
comparable corpus of non-translations in the target language. These components are
necessary to reliably capture and describe various translationese effects by com-
paring feature frequencies across three text types in each register: sources, targets
and reference texts. Methodologically, we combine multivariate analysis in
supervised and unsupervised ML settings and univariate statistical analysis to reveal
prominent translationese indicators and describe trends observed within and across
the registers. Our features include content-independent morphosyntactic features
that allow to abstract from topic and domain information as well as indirect lexical
indicators retrieved from language models learnt on separate and much bigger
register-comparable resources. Importantly, all features are shared by the two lan-
guages involved to enable placing all texts into the same multidimensional feature
space.
2.2 Translationese and Register
This research explores the translation properties that are observed in various reg-
isters. It is difficult to deny that language is not homogeneous. Language is a
combination of subsystems that are employed in specific communicative condi-
tions. One important dimension of language variation, distinct from domain sub-
languages, territorial or social dialects, has to do with the dominant communicative
function and the generalised type of the situation in which the textual activity takes
place. This type of variation is referred to as registers or genres depending on which
aspects of the communicative event are focused. David Lee, the author of one of the
text categorisation schemes in the British National Corpus (BNC), prefers to think
about these competing terms as ‘two different points of view covering the same
ground’(Lee 2001: 46). The term register signals that language material is
approached from the viewpoint of its internal properties (such as frequencies of
linguistic items), which form specific patterns of use predetermined by the com-
municative conditions (‘the context of the situation’) in which they occur. The
major situational factors are typically described following Halliday’s categorisation
into field, tenor and mode. Genres are understood as text categories more focused
142 M. Kunilovskaya and G. Corpas Pastor
on the text-external and functional parameters; they are text schemata licensed by
the culture and superimposed on the register. According to James Martin, ‘no
culture combines field, mode and tenor variables freely’(1992: 562). This approach
is in line with Michael Halliday’s interpretation of register (see Register Variation
chapter in Halliday and Hasan 1989) and is adapted in a number of corpus and
computational linguistics projects, especially based on the BNC (see Lijffijt et al.
2016; Neumann 2013; Santini et al. 2010; Sharoff 2018).
In translationese studies, it seems more typical to refer to the analysed text
categories as registers (see Diwersy et al. 2014; Kruger and Rooy 2012;
Lapshinova-Koltunski 2017 among other works). However, Delaere (2015) con-
sistently prefers the term ‘genre’to refer to the text categories of similar names and
granularity, because in her research these categories are explicitly annotated using
such non-linguistic characteristics (addressor, addressee, channel and communica-
tive purpose), following the methodology in Biber and Conrad (2009).
In the current research, we follow this interpretation of the contextual language
variation and refer to the four text categories under comparison (general domain
mass-media texts, popular-scientific texts, fiction, political-economic news com-
mentary) as registers.
Register is widely acknowledged as one of the major factors that influences the
properties of translations, along with the source language.
2
This is not surprising
precisely because of the strong SL pull in translations, given that ‘parallel registers
are indeed more similar cross-linguistically than are disparate registers within a
single language’(Biber 1995: 279). In a lot of earlier research, this is corroborated
as a by-product of a different research focus and/or as a result of observations from
manual analysis of some restricted corpus data. For example, a relatively
small-scale study based on half-a-million word corpus by Puurtinen (2003) indi-
cated that genre could be an important factor guiding translation choices. The
authors concluded that ‘subgenres of children’s literature …should be investigated
separately’(Puurtinen 2003: 403).
Xiao, He, and Ming (2010) report the construction of a register-balanced corpus
of translational Chinese and original Chinese texts after the FLOB sampling frame.
In their univariate analysis of several known translationese indicators, they show
that the features tested, including lexical density (STTR), mean sentence length,
conjunctions and passives frequencies, display ‘genre subtleties’in translation.
Our research can be compared to Kruger and Rooy (2012), who see the
investigation of the relationship between register and the features of translated
language as one of their main research goals. They performed univariate analysis
for seven features, which represented three translationese universals, to see how the
universals would play out within and across their six registers. In their research
design, explicitation, normalisation and simplification were operationalised with the
(1) frequencies of full forms (as opposed to contractions), that-complementisers,
2
Earlier studies that suggest that translationese is dependent on register are Steiner (1998), Reiss
(1989) and Teich (2003), among others.
Translationese and Register Variation …143
linking adverbials; (2) frequencies of coinages, loanwords and common lexical
bundles; and (3) values for lexical diversity and mean word length, respectively.
Their results provided limited evidence for universal character of translationese,
rather each register demonstrated its own pattern of analysed features. In a later
research using the same features, the levelling-out of registers, conceptualised as the
assumed reduced register variability in favour of a neutral middle register, was not
supported either (Redelinghuys 2016).
In recognition of the importance of register in translationese studies, researchers
pay special attention to the selection and annotation of the reference corpus of
non-translations: Castagnoli (2009) decided to build a new corpus from scratch,
Delaere (2015) re-annotated an existing resource, Kunilovskaya and
Lapshinova-Koltunski (2020) used a special corpus sampling strategy to extract
functionally comparable subsets from larger corpus resources.
The large-scale studies of translated registers that allow reliable application of
statistical methods or ML techniques are comparatively rare. There is a case study
in Diwersy, Evert, and Neumann (2014), based on a reasonably large
register-balanced bidirectional English and German corpus, but its contributions
were more of the methodological nature: they reported few findings that charac-
terised individual registers in translation, if any.
Delaere (2015) used the frequencies of linguistic items associated with the
general properties of texts such as formal/neutral language and native/borrowed
words to profile originally-authored and translated texts and test whether the
translators tend to conform to the observed TL norm. Her findings for five genres in
several language directions between Dutch, English and French generally con-
firmed the normalisation trend in translations and the impact of the genre and SL
factors, but there was no consistency in the results. The authors attributed this
inconsistency to incomplete metadata in the corpus and some unaccounted factors
that might govern translators’choices. The sparsity of the indicators and domain
disparities could also be confounding factors, given the lexical nature of the
operationalisations implemented and the relatively small size of each subcorpus
used in the study.
Unlike the previous study, which relied on predefined operationalisations of
some properties of translated texts like levels of formality, Lapshinova-Koltunski
(2017) employed hierarchical cluster analysis, an unsupervised ML method, and
represented English-to-German translations and German non-translations in seven
registers as feature vectors using eight lexico-grammatical patterns that were
inspired by register studies to see how much the properties of translations were
influenced by two factors―the register and the method of translation. Their features
are context-independent and characterise texts through ratios of, for example,
nominal vs. verbal parts-of-speech or through cumulative frequency values for
items expressing modality or evaluation among others. The results of the study
showed that the functional text type dimension dominated as a factor for some
registers but not others. This research, as well as an earlier research on the same
data using SVM classification (Vela and Lapshinova-Koltunski 2015), had its focus
on the comparison of human and machine translation across a range of registers.
144 M. Kunilovskaya and G. Corpas Pastor
They found that the two translation varieties were more similar between themselves
than any of them were similar to the register-comparable non-translations. In a later
work on the same data, they used part-of-speech (PoS) trigrams in a number of
binary text classification experiments to reveal and interpret features distinguishing
translated registers. They confirmed their earlier finding that ‘the genre dimensions
in translation variation is much stronger than that of translation method’
(Lapshinova-Koltunski and Zampieri 2018: 107). These three studies indicate that
human and machine translations are more similar between themselves than any two
translated genres, regardless the feature set used and ML approach chosen.
3 Methodology
In translationese research, the results are largely dependent on the features used to
represent the texts, including their selection and extraction. Features are usually
frequencies or ratios of linguistic items and phenomena, used to operationalise
various hypothesised translationese trends or to capture and measure translationese
effects in the bottom-up approach.
Another important factor is the type, quality and size of the corpus resources
used to produce data tables. As it is shown above, both parallel and comparable
components are required to be able to interpret quantitative differences between
translation and non-translations.
There can be various ways of looking at the data methodologically, ranging from
manual in-depth analysis of a few contrastive linguistic phenomena and/or statis-
tical significance testing to ML experiments, usually cast as text classification
problems or various types of factor analysis and computational linguistics methods.
While the previous research has reported some tried and tested approaches, they
leave a lot of room for development and exploration, especially if new research
questions are posed.
Unlike much of the related work, where register effects on translationese
properties are used as a backdrop for another primary research questions, the
current research employs ML techniques to compare the type and strength of
various translationese effects in several registers as well as to reveal the transla-
tionese indicators that might cut across all registers. This section has the description
of these three major components of our research design: features, data and methods.
3.1 Feature Sets
Similarly to Volansky, Ordan, and Wintner (2015), our features are not selected to
get the highest accuracy for the binary classification of originally-authored texts and
translations (translationese classification). We seek to investigate the variation in
translations along the register dimension in a linguistically interpretable way.
Translationese and Register Variation …145
In the literature, the types of features used to capture translationese in the ML
setting vary depending on the specific task. Translationese detection and SL
identification tasks almost exclusively rely on character, word, lemma, PoS or
mixed n-grams of various order
3
and most frequent lemmas (including function
words) or PoS.
4
A bold exception is the projects that aim at sentence-level detection
of translation direction (Eetemadi and Toutanova 2015; Sominsky and Wintner
2019). They leverage the aligned PoS information from source and target sides of
the parallel corpora to achieve the state-of-the-art results. Sominsky and Wintner
(2019) reported further improvements of up to 6% accuracy (at the expense of
interpretability) for four out of six tested language pairs on distributional
50-dimension pre-trained GloVe word embeddings used to represent words and fed
to a neural network of one bidirectional Long Short-Term Memory (BiLSTM)
layer.
The more linguistically orientated research, which aims to know more about the
linguistic specificity of translations, considers the feature selection the most chal-
lenging and creative part of the task. On top of the well-known and most-tested
translationese indicators (such as type-to-token ratio, content-to-function words
ratio, frequency of connectives/conjunctions and pronouns, ratio of contracted to
full forms, average sentence length, mean word rank), the authors suggest more
elaborately engineered features. For example, Arase and Zhou (2013) used the
frequency of discontinuous structures to capture ‘phrase salad’in MT.
Redelinghuys (2016) calculated readability scores, while Volansky, Ordan, and
Wintner (2015) operationalised the normalisation hypothesis with average
point-wise mutual information (PMI, one of the association measures used to detect
collocations) of all bigrams and ratio of repeated content words along with other
features. Lapshinova-Koltunski (2017) suggested a feature set, which included
features like frequency of evaluative patterns and degree of nominalisation (ratio of
nominal and verbal PoS). Some experimenting was done with the frequency fea-
tures based on parsed data: Ilisei et al. (2010) calculated ratio of simple sentences
and parse tree depth and Kunilovskaya and Kutuzov (2018) extracted and counted
syntactic relations tags from UD annotations of their corpora.
In our research the feature selection and engineering process was informed
(1) by the findings in the translation and translationese studies, including the
practical observations made in English-to-Russian translation textbooks, but never
tested empirically and (2) by the practices in the register studies and variational
linguistics on the assumption that translations could be viewed as a specific sub-
language, a third code (Duff 1981; Frawley 1984), based on the specificity of
distribution of the linguistic features. This is supposed to enable measuring the
cross-linguistic distance between the registers as well as between translations and
non-translations. This approach effectively means that our feature set is language
3
See, for instance, Baroni (2006), Kurokawa (2009), Arase (2013), Eetemadi (2015) and
Rabinovich (2016).
4
Some relevant studies are Popescu (2011), Koppel (2011) and Nisioi (2013).
146 M. Kunilovskaya and G. Corpas Pastor
pair specific and would require adaptation to be extended to other language pairs
(see such adaptation in Kunilovskaya and Lapshinova-Koltunski 2020). Besides,
our research design required that the features (3) should be shared by the languages
involved in the experiment. We also focused on (4) content-independent features to
reduce the noise from the topic and domain divergence between the parallel and the
reference corpora, which excluded the common bag-of-words models from our
options. Finally, we avoided (5) less interpretable features and (6) features that defy
reliable extraction based on our experience.
Unlike much of the previous research into translationese, overviewed in
Sect. 2.1, we do not assign features to the known translationese trends in the
top-down manner, but empirically establish their role in producing various trans-
lationese effects. The experimental setup in this study can handle irrelevant or
collinear features, and we use a reasonably high number of potential translationese
indicators to be able to distil the most useful ones through feature selection.
Our feature set is composed of two parts. First, it includes 45 morphosyntactic
features that were introduced in Kunilovskaya and Lapshinova-Koltunski (2019) to
capture human translation quality. We provide a brief overview of these features
below. For the full description of each individual feature, refer to Appendix. The
feature codes used in this chapter and the extraction details are given in the
Appendix alphabetically. Second, it comprises 11 abstract lexical features to reflect
the specificity of the lexical choice in translations.
The morphosyntactic features are extracted from the annotation performed
within the Universal Dependencies framework (Straka and Straková2017), using
models pre-trained on 2.5 versions of the EWT and SynTagRus treebanks for
English and Russian, respectively.
More than a third of these features (17) are the frequencies of the default UD
morphosyntactic tags (such as ccomp: clausal complements or sconj:subordinating
conjunctions) and their combinations (such as numcls: number of clauses per sen-
tence counted as the number of relations tagged csubj, acl:relcl, advcl, acl, xcomp in
one sentence); when extracting PoS tags for various types of pronouns and other
closed word classes, we used lists to filter out noise. The other third of the features
(16) involved custom rules and extraction patterns, detailed in Appendix. These
include lexical type-to-token ratio, modal predicates, passives, mean dependency
distance (mdd, which represents ‘comprehension difficulty’defined as ‘the distance
between words and their parents, measured in terms of intervening words’(Jing
and Liu 2015). In developing these features we took into consideration the
description in (Evert and Neumann 2017; Nini 2015) for English and in (Katinskaya
and Sharoff 2015) for Russian. Further on, the cumulative frequencies for the four
semantic types of connectives, epistemic markers and adverbial quantifiers are
extracted using predefined lists compiled from the literature (see more details on the
items selection, academic sources, extraction and disambiguation in Appendix).
Generally, our UD-based indicators include morphological forms (e.g. non-finite
forms of verbs), syntactic relations (e.g. clausal complements), syntactic functions
(e.g. modal predicates), word classes (e.g. pronouns, discourse markers). The
extraction quality of these features largely depends on the quality of the UD
Translationese and Register Variation …147
annotation: for v2.5 mean accuracy on raw text is reported at 93.3/97.8 for universal
PoS, 94.2/93.5 for morphological features and 77.0/85.0 for labelled dependency
attachment for English/Russian, respectively.
5
For this project we implemented 11 additional features to approach transla-
tionese at the lexical level as well. It is obvious that we cannot rely on frequencies
of individual character or word n-grams in our cross-lingual setting. Besides, it is a
known fact that sparse vectors of string features do not generalise well across
domains (Eetemadi and Toutanova 2015). Instead, we used language model
(LM) perplexities and calculated ratios of n-grams from top and bottom frequency
quartiles, using the KenLM toolkit (Heafield 2011) and Quest ++ utilities (Specia
et al. 2015). These features are used for the analysis of translationese in the research
projects, which target translation quality (see Karakanta and Teich 2019 and
Quest ++ feature set). We hypothesise that translated texts might have a diverging
lexical composition in terms of ratios of n-grams from high- and low-frequency
bands and sentence perplexity scores due to unseen sequences induced by the
translation process. Our text-level lexical features include:
•mean target sentence perplexity score from the 3-g language models trained on
large register-comparable corpora (see 3.2.2 for details);
•standard deviation value for the above sentence perplexities to account for
possibly uneven lexical complexity of sentences in the translated texts;
•ratio of uni-, bi-, trigram that were not seen in the n-gram lists from the reference
corpora;
•ratio of n-grams from the 1st frequency quartile (low-frequency items)
•ratio of n-grams from the 4th frequency quartile (high-frequency items)
To produce these features, we collected separate language resources for each
register making sure they do not intersect with the smaller reference corpora
included in our experimental data to exclude unfair bias for these features. Before
learning LMs and generating n-gram lists, all corpora had been lemmatised and
PoS-tagged with UDPipe (Straka and Straková2017) to get lempos representation
(e.g. as_SCONJ i_PRON look_VERB up_ADP ._PUNCT). This is required
because Russian is a morphologically rich language; English is pre-processed for
higher consistency and comparability.
As a result of feature extraction, each text in our data was represented as a vector,
where individual components corresponded to the value of each feature for this
text. The dataset, used in the experiments, can be thought of as a table, which has
texts in rows and features in columns. Note that prior to the experiments, the values
of each feature were standardised to get the distribution with a mean value 0 and
standard deviation of 1. This helps to ensure that all features have the variance of
the same order, and each feature makes the same contribution to the differences
observed, regardless of large discrepancies in real values between some indicators.
5
http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_20_models.
148 M. Kunilovskaya and G. Corpas Pastor
3.2 Research Corpora
This research relies on several parallel and comparable corpora to explore the
linguistic properties of texts translated from English into Russian by professional
translators across a variety of registers. We distinguish between the corpora used to
conduct experiments (data) and the corpora used to learn language models and
produce n-gram frequency lists (linguistic resources).
All corpora were put through the same pre-processing pipeline (spelling unifi-
cation, text size normalisation, deduplication, noise filtering), annotated with
UDPipe and converted to PoS-tagged lemmas (lempos format).
3.2.1 Data
The selection of registers for this project was limited by the availability of the
English-Russian parallel and comparable corpora that would store texts of rea-
sonable size and structure. We considered a wide variety of the available parallel
corpora, including web corpora (Yandex 1 M-token parallel corpus, Parallel
Corpora for European Languages), United Nations corpus, corpora of subtitles and
Wiki Titles, TedTalks corpora and mozilla transvision corpus of technical trans-
lations. But the units of storage in these corpora were often limited to one sentence
or would include a lot of non-textual information and tables. TedTalks transcripts
and subtitles have specific translation processes behind them that can unfairly
influence the frequencies of our features. It is also more difficult to make
assumptions about the translation quality for these corpora and compile
non-translated comparable corpora for them.
We focused on the four registers: general domain mass-media texts,
popular-scientific texts, fiction and the news commentary texts in the political and
economic domain. All translations included in the experiments are published. We
only selected the corpora that store texts with respect to their natural text bound-
aries, which allows the collection of text-level statistics. The parallel subcorpora are
document-level and sentence-aligned. The global sources of data in this project can
be described as follows.
1. Mass-media parallel corpora include data from the three major sources: a quarter
comes from the parallel component of the Russian National Corpus (RNC)
6
and
the rest of the data were manually collected or crawled from InoSMI.ru and
BBC.com/russian (2018–2020).
2. Popular scientificparallel corpus is self-compiled from a dozen of full-length
English books on a range of subjects including biology, physics, sociology,
history, anthropology, robotics, medicine, and their published translations into
Russian from 1999 to 2016 period. This corpus is now included into the RNC
6
https://ruscorpora.ru/.
Translationese and Register Variation …149
parallel resources. While the number of observations is small, the selected unit
of storage is a chapter or a part of the book.
3. The parallel data for fiction is entirely from the RNC parallel component. It
includes 149 source texts of various length and literary genres, but mostly
novels representing over a hundred of authors from Dickens to Rowling.
4. Parallel political and economic articles (commentary) are extracted from the
WMT News Commentary corpus (v.15),
7
which contains political and economic
commentary crawled from Project Syndicate website.
The originally-authored Russian texts to be used as the reference for the former
three registers were randomly sampled from the respective register subcorpus of the
main 500-million RNC and for the last category―from the 300-million contem-
porary Russian newspaper corpus, included in the RNC monolingual resources.
Table 1has the description of the pre-processed and annotated parts of our
register-balanced corpus including the parallel and comparable monolingual com-
ponents. For the parallel data we report the size on the SL side only.
In total we have 3349 documents in two languages, labelled for four registers
and three types (sources, targets, reference).
3.2.2 Linguistic Resources
The resources for LM training in all registers, except the English news commentary,
come from the British National Corpus (BNC) and the Russian National Corpus
(RNC). We relied on the available metadata to ensure maximum comparability with
the parallel data in terms of intended audience, text production time and commu-
nicative function. The English political and economic commentary reference texts
are collected from the WMT News Commentary corpus outside the
English-Russian parallel data. Note that these resources exclude the random
Table 1 The macro-corpus used for research purposes (k=thousand, m=million)
Type of data Words Sentences Documents
general media parallel 731 k 31 k 525
reference 625 k 33 k 448
popular science parallel 1 m 42 k 112
reference 1 m 46 k 101
fiction parallel 11 m 564 k 149
reference 12 m 706 k 200
commentary parallel 301 k 12 k 347
reference 276 k 13 k 334
7
http://www.casmacat.eu/corpus/news-commentary.html.
150 M. Kunilovskaya and G. Corpas Pastor
samples used as reference data and described in Table 1. The general shape of the
resources after pre-processing and annotations can be found in Table 2.
We will indicate that the mass-media items in the BNC do not observe true
document boundaries but are in fact text chunks of varying length. However, it is
irrelevant for the purposes of building LMs and n-gram lists.
3.3 Methods
Our methodology combines the data representation and visualisation approaches
which were shown to be effective for the study of translations in Evert and
Neumann (2017) and the idea that in revealing or measuring translationese effects,
the distance between the source and target languages (or, in our case, registers) has
to be taken into account. We develop the general approach tested in Kunilovskaya
and Lapshinova-Koltunski (2020) on one register for two language pairs.
To represent texts in our data we generate feature vectors, where each compo-
nent has the value for a particular linguistic parameter. With the exception of the
LM perplexity scores, these values are the frequencies or ratios of a targeted lin-
guistic phenomenon, captured through a set of PoS tags or a syntactic pattern. For
features based on the search lists, the values are cumulative frequencies of all items
on the respective list. For n-gram counts, we used an empirically established fre-
quency threshold of 10, which means that we ignored the n-grams with a frequency
lower than 10. This measure helps to avoid zero values for bigram and trigram
ratios. Given that our features are the same for all text categories and text types, this
representation effectively puts them in a shared feature space. The extraction details
are given in Sect. 3.1 and in Appendix.
We resort to PCA, an unsupervised ML technique, for dimensionality reduction
to present our observations in scatter plots and visually estimate whether our fea-
tures reflect the ontological text categories and types. The visual impressions are
verified by the results of text classification. In all experiments we rely on the linear
SVM algorithm, set to the default scikit-learn parameters (C = 1.0, degree = 3,
gamma = auto). The algorithm is fed with the feature vectors that have been centred
Table 2 Corpora used to train language models and generate n-gram lists
Language Words Sentences Documents
general media en 3.9 m 177 k 100
ru 129 m 6.9 m 226 k
popular science en 17.7 m 682 k 528
ru 1.9 m 93 k 378
fiction en 18.6 m 1.2 m 431
ru 37.6 m 2.6 m 580
commentary en 5.9 m 237 k 8.7 k
ru 5.7 m 252 k 9.5 k
Translationese and Register Variation …151
around the mean and scaled to unit variance and is run in the ‘balanced’mode to
offset the unequal number of observations in the training classes. We report the
results in the tenfold cross-validation setting to reduce the possible biases of any
single held-out test set.
In accord with our research questions, given in Sect. 1, the text classifications
are designed to capture the following general properties and phenomena:
•translational status: a binary classification for each register;
•register variation: a 4-label classification for non-translations in each language;
•standardisation effect: a 4-label classification for translated texts only.
To determine the position of each translated register with regard to the sources
and TL non-translations, we average the real-valued vectors across each of the three
text types and calculate the Euclidean distances (a square root from the sum of
squared differences between the corresponding dimensions of the two vectors)
between them. We rely on the Euclidean distance (as opposed to cosine similarity,
for example) because in this experiment we use unscaled vectors and the magnitude
of the values in each dimension matters. The differences between the three mea-
surements, which can be pictured as triangles, demonstrate the relative proximity
(similarity) of the translated texts to the originally-authored registers in the two
languages. The idea to measure linguistic (morphosyntactic) distances between
languages for the purposes of translationese studies is not new. To this end,
Nikolaev et al. (2020) computed the cross-linguistic congruence index as the pro-
portion of matching universal PoS tags and dependency labels for all manually
aligned content words in a parallel corpus. They acknowledged that there was no
established procedure to achieve it.
The explanatory analysis of the linguistic specificity of translations in each
register is based on the best translationese indicators, i.e. the top N features that can
be used by the ML learning algorithm to differentiate the classes with the minimum
loss in the classifier performance. Our experimental results indicated that the best
performance for the top 10 and top 20 features was returned by the Recursive
Feature Elimination (RFE) feature selection algorithm, which internally used
Support Vector Regressor (SVR) with the default scikit-learn settings. The same
approach was used to reveal register contrast indicators that were necessary to
demonstrate the amount of intersection between the translationese and cross-lingual
contrast features.
Finally, we perform a succession of the univariate analyses to establish which
features contribute to various translationese effects that we distinguish in this study
following a procedure described below. In all experiments we used the two-tailed
T-test for samples with unequal variance and quantified the effect size of the
differences with Cohen’sd. First, we identify the features that have significant
differences between translations and non-translations (tgt, ref): these are transla-
tionese indicators. Then, we establish whether there are differences between the two
cross-linguistic registers (src, ref) with respect to a given feature (the language gap).
Finally, we compare the average frequency for the feature in translations with those
152 M. Kunilovskaya and G. Corpas Pastor
in the source and target languages to determine how it relates to these values
(greater or smaller).
Combinations of these tests outcomes yield the feature sets for the following
translationese effects:
1. shining-through effect: translationese features in the language gap, i.e. we
observe significant differences between translations and non-translations and
between English and Russian non-translations; and the frequencies of features
from translations are smaller than in English but significantly greater than in
non-translated Russian (src > tgt > ref) or greater than in English but smaller
than in Russian (src < tgt < ref);
2. anglicisation: translationese features demonstrating frequencies outside the
English extent of the significant language gap;
3. SL/TL-independent translationese: translationese features with significant dif-
ferences from both languages and no language gap;
4. over-normalisation: translationese features demonstrating frequencies outside
the Russian extent of the significant language gap;
5. adaptation: features that have significant differences for the two languages, but
not translationese features, i.e. their frequencies are adapted to the TL norm.
This procedure is also supposed to reveal features that are useless for our pur-
poses: the feature that has the same frequencies in translations and non-translations,
and also do not distinguish the languages.
4 Results
In this section, we first report the results of the two classification experiments that
test the ability of our feature sets (1) to distinguish translations and non-translations
in each register, (2) to capture the register variation in the originally-authored texts
in each language. We also look at the performance of the register classification on
the translated registers to check whether the register distinctions are diluted by the
translation process. If the translated registers are more difficult to classify, we can
confirm the levelling-out hypothesis. The second paragraph demonstrates how the
translated registers are positioned against comparable non-translations in both
languages (src, ref) based on the Euclidean distances in our setup. We complement
the spacial representation of translated and non-translated registers with histograms
for values on the strongest PCA dimension, which appears to mostly capture reg-
ister variation in our data. Finally, we describe the subsets of features that are
revealed through feature selection and comparative frequency analysis and repre-
sent several translationese effects. Feature analysis is performed to explain the
observed specificity of each translated register with regard to their sources and
reference non-translations.
Translationese and Register Variation …153
4.1 Translationese and Register Distinctions
For a preliminary investigation of the data, given our features, we visualised the
distinctions between all text types on the full feature set and on its morphosyntactic
and lexical parts. For example, Fig. 1has a scatter plot, where each document is
represented by the values on the first two PCA dimensions, i.e. the result of the
dimensionality reduction of the 45-dimensional morphosyntactic vector. Unlike
lexical features (not shown for the consideration of space), the morphosyntactic
features manage good separation of the registers and the two languages. It seems
that the register variation is found on Dimension 1, which explains the most
variance in the data, while Dimension 2 (shown on the vertical axis in Fig. 1)
captures the language contrast. The lexical features are not able to achieve this
representation of data on the most prominent known properties of the texts: they
squeeze all variance into the first dimension. It means that in terms of ratios of
high-frequency and low-frequency n-grams the similarity between registers from
different languages is stronger than the differences between languages. This
observation is confirmed by the language contrast classification (English vs.
Russian original texts) results: for morphosyntax 100% accuracy can be achieved
Fig. 1 Values on the first two PCA dimensions derived from the morphosyntactic features
154 M. Kunilovskaya and G. Corpas Pastor
on just 3 features (aux, aux:pass, parataxis), while the 11 lexical features returned
only 85%.
The concatenation of the two feature sets captures the register distinctions on
Dimension 1 and language distinctions on Dimension 2 more clearly (see Fig. 3).
However, the distinctions of translations and non-translations, required by the
first step in our methodology, are clouded. To bring them to the fore for closer
exploration, we tried to cast the full feature vectors of size 56 for translations and
non-translated Russian texts to a bidimensional space by PCA and produced a
scatter plot of the resulting data. The independent subplots in Fig. 2position the
texts in each register according to the values received on the first two principle
components.
It can be seen that translations are shifted away from the non-translations,
especially in general mass media and news commentary. It means that our features
do register some divergence of translated Russian from the expected TL norm in
these registers represented by non-translations. Admittedly, the visual impressions
are more subtle in the other two registers. Note that PCA is unsupervised: it is
unaware of any text types that are colour-coded in the plots. Besides, PCA reduces
the 56 dimensions to just two, necessary to plot the data, which inevitably leads to
the loss of information and distortions. That is why we verify the visual impressions
Fig. 2 Differences between translations and non-translations by register
Translationese and Register Variation …155
with a series of binary translationese classifications using SVM. The classification
results confirm that PCA visualisations can be, indeed, misleading, because the
registers with seemingly different visual distinctions (fiction and news commentary)
achieve the same high classification accuracy, while the accuracy for general mass
media is lower, in contrast with what is observed in Fig. 2.
The cross-validation results are presented in Table 3, which shows SVM per-
formance on the translationese classification, taking into account accuracies and
macro F1 scores. On the full feature set in three registers, SVM achieves the
accuracy of over 95%, while for mass-media texts it is 87%, which is still rea-
sonable high. We have fairly balanced classes in all registers, so the chance level
never exceeds 50%.
The classification experiments on morphosyntactic and lexical feature sets
separately indicate that the result in the 56 features column (see Table 3) is mostly
produced by the morphosyntactic features. If lexical features are eliminated the
classifier performance does not degrade much in any registers: the loss amounts to
1% and 2% in accuracy for fiction and commentary at most. However, switching to
just lexical features results in the drops in performance ranging from minimum 7%
(news commentary) to maximum 17% (popular science). It means that for the
translationese classification (1) news commentary relies on the lexical features most,
i.e. they demonstrate the highest divergence from non-translations; (2) for popular
science structure is most important, i.e. translations differ from non-translations in
morphosyntax; (3) in general media both feature sets perform the worst, possibly
because of the higher variation in the respective subcorpora observed in Fig. 3.
Secondly, we are interested in finding out whether our features model the reg-
ister diversity in both non-translated languages well. In Fig. 3we plotted the
originally-authored texts in the two languages, represented by their values on the
first two PCA dimensions generated by the PCA transform of the full feature vector
of size 56. Most variance is explained by Dimension 1, which captures register
variation. Texts from different registers seem to occupy specific areas along the
horizontal axis, especially in Russian. The second dimension has the clear sepa-
ration of the two languages. The plot in Fig. 3also indicates that some eponymous
registers are closer together across languages than others. For example, fiction and
news commentary seem to be more similar along the vertical ‘language contrast’
dimension than general mass-media texts and popular science.
Table 3 SVM performance on the translationese classification in each register
N texts 56 features 45
morphosyntax
11 lexis
general media 973 87% 0.872 87% 0.869 75% 0.750
popular science 213 98% 0.977 98% 0.981 76% 0.754
fiction 349 95% 0.953 94% 0.936 77% 0.759
commentary 681 95% 0.947 93% 0.934 88% 0.879
156 M. Kunilovskaya and G. Corpas Pastor
Popular science has the most expressed register differences in the
cross-linguistic perspective of the four registers (notice the horizontal mismatch of
the respective blue areas in the plot). Mass-media texts display a lot of in-category
variation along the horizontal ‘register’axis, especially in Russian. Judging by the
upward and downward shifts of the respective clouds, this register passes some
register distinctions on to Dimension 2, which ideally would capture only the
language contrast. PCA on our features also struggles with distinguishing popular
science and news commentary in English.
The classification results confirm that our features separate the four registers
fairly well. For all 56 features, the SVM classifier, which predicted the four classes,
returned 97% accuracy for each languages (F1-score 0.966 and 0.974 for English
and Russian respectively). The chance level is 30% for English and 34% for
Russian, with correction for imbalances between the four classes. In line with the
visual impressions, most classification errors were between mass media, com-
mentary and popular science in English and between media and fiction in Russian.
As expected in this experiment, the lexical features performed better: the 11
features were only 1% worse than 56 for English, while for Russian the decrease in
performance amounted to 4%. The morphosyntactic features (45) alone were able to
achieve only 78% and 81% accuracy for English and Russian, respectively. We can
tentatively conclude that in our setting the register distinctions in English are
conveyed through lexis to a greater extent than in Russian, where registers have
more morphosyntactic specificity.
Finally, we tested whether the register distinctions in the SL are flattened out by
the translation process―an assumption made by the levelling-out hypothesis (the
tendency of translations to gravitate towards unmarked features in contrast to
non-translated texts (Baker 1996)). The plot in Fig. 4shows the difference in the
Fig. 3 PCA representation of registers in non-translations in English and Russian (56 features)
Translationese and Register Variation …157
localisation of the registers, some of which are even better separated than in the
non-translated Russian (compare to the bottom part of the plot in Fig. 3). The
translation process seems to import some confusion between popular-scientific texts
and news commentary, on the one hand, and reinforce the separation between these
two and mass media and fiction, on the other.
In this experiment, the SVM achieved the average tenfold cross-validation
accuracy of 99% with a macro F1-score of 0.982 on the full feature set.
Interestingly, the errors in the contingency table were between other classes than in
non-translated registers: they were predictably between news commentary and
popular-scientific texts (same as in the classification for English originals), rather
than between mass media and fiction (as was the case in the classification for
Russian originals).
Another intriguing observation is that the importance of lexical features for
predicting translated registers increased compared to the texts originally written in
Russian. The accuracy of register classification on the lexical feature set went up
from 93 to 99% and was better than on all the 56 features. At the same time, the
morphosyntax of translations introduced some noise: the classification on the 45
features from UD annotations for translation was 1% worse than for the texts
Fig. 4 Translated registers in Russian: PCA transformation of 56-dimensional feature vectors
158 M. Kunilovskaya and G. Corpas Pastor
originally written in Russian (80 vs. 81% accuracy). It indicates that the translation
process does interfere with the target language register system on the structural
level, but in terms of lexis translators tend to conform to the conventional distri-
butions seen in the respective register. Table 4systematises the results of the 4-class
register classifications run on the three feature sets for each type of text in this
project.
4.2 Euclidean Distances Between Translations
and Non-Translations
To measure the apparent change of register properties in the translated language, we
calculated the Euclidean distances between the register vectors for each text type
(sources, targets, references). They were produced by averaging the text vectors
across each category. The resulting distances are shown in Fig. 5as a scale of the
real values indicated in the diagrams. While lexical features did not contribute much
to defining the specificity of translations, they were not used in measuring these
distances. Besides, due to the drastic differences in the magnitudes between
Table 4 Register distinctions in the original texts and translations for different feature sets
(accuracies and macro F1 scores)
N texts 56 features 45
morphosyntax
11 lexis
English sources 1133 97% 0.966 78% 0.789 96% 0.955
Russian reference 1083 97% 0.974 81% 0.831 93% 0.934
Russian targets 1133 99% 0.982 80% 0.806 99% 0.983
Fig. 5 Euclidean distances between the text types in each register
Translationese and Register Variation …159
morphosyntactic and lexical features the latter overshadowed the former in this
distance measure.
The translations in each register demonstrate some differences in how they are
related to their sources and the expected target language norm. The mass media and
popular science texts seem to have the most similar translationese properties,
though the scale of differences is greater in the former. This generalised repre-
sentation of translations from the news commentary subcorpora makes translations
appear to be shifted more towards the TL than in the previous two registers, but at
the same time the translations are more distinct from either of languages (this is
indicated by the greater elevation of the tgt apex over the src-tgt plain and can be a
sign of the greater amount of SL/TL-independent translationese in this register).
Finally, fiction stands out as demonstrating an uncommon translationese shape: the
diagram indicates the prevalence of adaptation or over-normalisation over
shining-through effects. Note that the distances between originally-authored texts
(src and ref in Fig. 5) replicate the visual results from Fig. 3.
As an additional sanity check, we computed the same measure for the random
halves of the reference corpora: the average distances over 10 iterations range from
0.169 (media) to 0.712 (fiction). This confirms that translations in Russian are
systematically different from the texts in the same register originally written in
Russian.
The peculiarities of translationese flavours in various registers are best captured
on the PCA ‘register’dimension (Dimension 1) obtained from the full feature set
for all texts in this project (see Fig. 6). The register properties of translations (solid
coloured lines) do not necessarily replicate one language or the other, and the
similarities between translations and non-translations can be seen under various
register contrast conditions. The greatest mismatch of the cross-linguistic registers
is seen in general media and popular science, but in the former translations tend to
be in the language gap, and in the latter they appear to reproduce the TL norms. In
fiction and news commentary register conventions seem to be most similar in
English and Russian, and yet translations either faithfully coincide with these
conventions or deviate from both.
The representations in these plots should not be taken literary, however. They do
not account for the distinctions captured on the other PCA dimension and are based
on the crude 2-dimensional transformation of the full feature vector. Contrary to the
visual impression, translations are easily distinguishable from non-translations in all
registers (Table 3).
To test Biber’s claim that registers can be more distant intra-linguistically than
cross-linguistically (Biber 1995: 279), we used the same approach to measure
pairwise distances between registers in non-translated English and Russian. The
results in Table 5, considered together with distances between src and ref for each
register in Fig. 5, support this claim. In both languages fiction is more isolated from
other registers structurally, especially in English, while cross-linguistically it returns
the smallest distance of 1.663.
160 M. Kunilovskaya and G. Corpas Pastor
4.3 Translationese Effects and Features
In this paragraph we explore the specificity of translationese in each register
through feature analysis. The results of the procedure based on the univariate
analyses for tgt-ref (translationese), src-ref (language gap) and src-tgt (proximity to
sources) are presented in Table 6. It aims to associate our features with the trans-
lationese effects described in paragraph 3.3. For the consideration of space, the table
lists the 20 best translationese indicators in each register. In brackets we indicate the
Fig. 6 Kernel Density Estimation (KDE) for the values on the PCA Dimension 1 (56 features)
Table 5 Euclidean distances between intralinguistic registers based on structural properties
(values for English are under the diagonal; values for Russian are above the diagonal)
general media popular science fiction commentary
general media 1.522 3.070 2.274
popular science 0.250 4.558 0.913
fiction 5.791 5.640 5.273
commentary 0.587 0.693 6.137
Translationese and Register Variation …161
Table 6 Features associated with translationese effects (based on univariate analysis of 56 features)
shining-through anglicisation SL/TL
independent
over-normalisation adaptation useless
General
media
but, nnargs, relativ, interrog, whconj, neg,
parataxis,comp, uoov, trioov, advers (23)
xcomp, acl,
sconj
(4)
epist,
passives (4)
- (9) pverbals,
lexdens,bifreq,
bioov
(16)
- (0)
Popular
science
nnargs, relativ, deverbals,copula,
whconj, aux,parataxis, nsubj:pass, infs,
mpred (29)
xcomp
(2)
passives,
sconj
(5)
tempseq, simple, acl
(8)
caus, advers,
mdd
(10)
epist (2)
Fiction nnargs, tempseq,pverbals, whconj, neg,
ppron, parataxis, ccomp, interrog (18)
- (0) epist, sconj
(3)
xcomp, simple,
mdd
(10)
bioov, bifreq,
finites, uoov,
trifreq, lexdens
(23)
deverbals
(2)
Commentary but, relativ, aux,parataxis,comp, sup,
infs,advers (20)
epist,
passives,
sconj
(7)
ccomp
(4)
finites, bifreq,
bioov,lexdens,
uoov, trioov
(20)
lexTTR, ppron
(4)
- (1)
SHARED parataxis (6) - (0) - (0) - (2) - (0) - (0)
Note that the three translationese features (sconj, epist, parataxis) shared between the lists of most important translationese indicators for our four registers,
given in Table 6, are outside of the top 10 translationese indicators. Two of them (epist, sconj) are associated with different translationese trends in different
registers
162 M. Kunilovskaya and G. Corpas Pastor
total number of features (out of 56) that fall with the respective translationese effect
according to the frequency analysis. The bold font indicates the features that are
among the 20 most important register contrast indicators in the respective
cross-linguistic register classifications. In all four cross-linguistic register classifi-
cations (media_src vs media_ref, fiction_src vs fiction_ref, etc.), the accuracy on the
selected features is 100%.
To identify the best translationese and the best register contrast indicators
mentioned above, we relied on the Recursive Feature Elimination (RFE) algorithm
in scikit-learn, a Python library. In effect, this algorithm performs an ablation study
on a given feature set by recursively pruning the least important features in the
multivariate setting, based an external estimator (SVR in our case). The univariate
approach to feature selection based on ANOVA (SelectKBest algorithm in
scikit-learn) returned a higher loss in classification performance for all experiments:
on average the classification on the 20 best ANOVA features performed 2.9%
worse than on the full feature set. For RFE-SVR this loss in the same experiments
was only 0.9%. However, the two feature selection algorithms demonstrate con-
trasting performance on popular-scientific texts, where ANOVA is better, and on
fiction, where the RFE 20 features do well, while ANOVA features demonstrate
5.8% decrease in performance on the F1 score. It indicates that in the first case the
multivariate analysis approach fails to reveal meaningful correlations between the
features frequencies, while for fiction the discovered patterns explain the difference
between translations and non-translations better than mere univariate comparison of
features. Nonetheless, the intersection between the 20 best indicators, returned by
RFE and ANOVA, ranges from 9 to 13 features for different experiments.
We should reiterate here from Sect. 3.3 that ‘adaptation’and ‘useless’sets
include features that are not translationese indicators per se, because there are no
statistically significant differences for their frequencies in translations and
non-translations. Nonetheless, they are not irrelevant for characterising translations.
As we will see below they are also important for the machine classification.
It can be seen from Table 6that fiction has the minimum number of
shining-through features (18) and the maximum number of over-normalised
(10) and totally adapted features (23) together, which explains the shape of the
triangle for fiction shown in Fig. 5and the matching lines in Fig. 6.
News commentary is peculiar for having the maximum number of anglicised
(7) and over-normalised features (20). It makes the translated texts in this register
stand out as being more distinct from both SL and TL, indicated in Fig. 5as a
greater elevation of the translations apex over the src-ref plain and in Fig. 6by the
location of the translations outside the area shared by sources and reference.
Another immediate observation is that the registers tend to have no shared
features for the suggested translationese effects, except shining-through and
over-normalisation. However, even these effects seem to be achieved through
widely different sets of features: only 6 features are shared among the average of 23
features for shining-through (nnargs, relativ, whconj, parataxis, interrog, mpred)
and there are two shared over-normalisation indicators (possdet, correl).
Translationese and Register Variation …163
It is also clear from Table 6that, in terms of the number of features,
shining-through is by far the most important type of deviation from the expected
norm in translation.
We failed to detect any pattern in the relation of the features prominent in
cross-linguistic register classifications (in bold) and the features important for the
translationese classification (named in Table 6). Some of the contrastive register
features are adapted to the TL norms and some are carried over from the SL.
The lists in Table 6should be taken with caution, though. One limitation is that
some features have negligibly small values and calculations for them are less
reliable. For others, the differences in frequencies can be significant but the effect
size is small. Besides, the impact of some feature sets associated with a given
translationese effect can be comparatively small in the classification task, despite
their size.
To verify the observations from the univariate analysis, we extracted the abso-
lute weights of the features associated with each effect for each register from the
SVM translationese classifier, and calculated the mean and standard deviation
(SD) for these weights. Feature weights from a linear SVM classifier can be used to
identify the features that contributed most to the classifier decision. This approach is
known to be reliable in feature ranking (Chang and Lin 2008) . Additionally, we
looked at the effect size (measured as Cohen’s d) for the features with significant
differences in frequencies between translations and non-translations (at p < 0.05).
We report the findings for the most prominent trends by register in Table 7.
It can be seen from Table 7that the effect size in the last column did not correlate
with the classifier weights. Some features with the observed greater magnitude of
Table 7 The most prominent translationese effects in each register (in the order of importance
based on the classifier weights)
effect N
features
Mean
weights
SD Cohen’s
d
General media anglicisation 4 0.645 0.210 0.851
shining-through 23 0.348 0.395 0.232
adaptation 16 0.325 0.316 –
Popular science SL/
TL-independent
5 0.243 0.137 0.063
adaptation 16 0.223 0.118 –
shining-through 29 0.183 0.161 0.079
Fiction shining-through 18 0.483 0.371 0.274
over-normalisation 10 0.451 0.387 0.099
adaptation 23 0.398 0.242 –
News
commentary
adaptation 4 0.662 0.280 –
anglicisation 7 0.583 0.380 0.601
shining-through 20 0.553 0.302 0.361
over-normalisation 20 0.449 0.292 0.200
164 M. Kunilovskaya and G. Corpas Pastor
differences were not selected by the algorithm as important. The comparison of the
performance of the two feature selection algorithms, given above, shows that from a
machine point of view finding patterns in the data is more effective than relying on
separate features in most cases. It is not clear, however, which translationese effects
are more visible (if any) to a human user.
5 Register-Based Translationese Varieties
We have seen that professional translations deviate from non-translations in the TL
in all registers, which is particularly noticeable on the structural level. These
deviations accommodate a number of trends, including shining-through,
over-normalisation and adaptation.
The size and the combination of the translationese effects is register-specific,
especially if we consider the associated sets of features. Our registers have just one
intersecting translationese indicator in the top 20 most important translationese
features (parataxis). It captures one strong and universal trend across our registers
in translations―to spot more introductory and parenthetical elements and
non-linear syntax. In general, the lexical features perform much worse than the
structural (morphosyntactic) ones, with the difference in accuracies of the transla-
tionese classifications ranging from 22% (popular science)to5%(news
commentary).
As for the translationese effects, shining-through is the strongest trend in all
registers, judging by the number of features identified as such and by their weights
in the classifier. It is complemented by tendencies with less features, but sometimes
higher prominence, to create a unique linguistic make-up for each category,
described below.
1. In general media the strong pull towards the SL is emphasised by anglicised
features and is to an extent counter-balanced by the fully adapted features. The
prevailing trend is still to exploit the SL patterns where possible. On the one
hand, it is understandably hard for translators to assimilate the considerable
cross-linguistic distance in this register. On the other hand, the expected TL
norm is less defined in Russian mass-media corpus than in the other registers
(note the broad spread of the media texts in Russian in Fig. 3).
2. Popular scientifictranslations have the record number of shining-through
indicators, but a third of them are lexical features that do not contribute much to
the translationese classification according to the classifier weights and the
analysis above, particularly in this register. The prevailing trend is towards
adaptation, which is reasonable, if we bear in mind a clearer delineation of this
register in the TL. This is the only register where the SL/TL-independent
translationese features are important for the classifier. Notably, this register has a
significantly lower frequency of passives and significantly higher frequency of
Translationese and Register Variation …165
subordinate conjunctions than in either original English or Russian, without a
cross-linguistic contrast for this feature.
3. Fiction has the least shining-through indicators, and yet, according to the
classifier, these features rank high in importance. The second strongest tendency
is over-normalisation (or russification). The pull towards the TL norm is rein-
forced by the considerable input from the record number (23) of fully adapted
features. This register appears to be the most Russian-like in translation.
4. In news commentary the few fully adapted features are assigned the biggest
weights. We will highlight that this register has the largest list of
over-normalised features (20) with relatively high weights. The other two effects
with comparably high average feature weights are anglicisation and
shining-through. It looks like this register is sharply torn between the two
languages.
The suggested feature sets are also fairly reliable for defining the contrastive
properties of the registers. They can be used to distinguish the four text categories
with 97% accuracy. However, the importance of morphosyntactic and lexical fea-
tures is reverse compared to the translationese classification. The lexical features
outperformed morphosyntax in register classification. Besides, we were able to
capture less morphosyntactic variation across English registers than across their
Russian counterparts. The translated registers exhibit clearer register distinctions
than the comparable TL non-translations, especially on the lexical level. However,
using morphosyntactic features only, it is more difficult to predict registers in
translations than in non-translations. It means that on the structural level the
translated registers are a bit less well-defined than non-translations in the TL (see
Table 4). It indicates that the translation process does not level out the distinctions
between the registers. Additionally, one can claim that the register conversions are
exaggerated and amplified, which leads to (1) higher similarity of translated texts
from one register and/or to (2) greater distances between the registers.
We put these two hypotheses to a quick test by (1) comparing the averaged
distance from centroid (corpus average vector) to each text vector for translated and
non-translated registers in Russian (‘degree of homogeneity’measure) and by
(2) measuring the Euclidean distances between the translated registers (and use the
distances in Table 5for reference).
These experiments show that (1) translations are less diverse than their
non-translated counterparts in all registers; (2) the second hypothesis holds only for
translated fiction, which is even stronger isolated from the other registers than in
non-translations (see Fig. 4), but not for the other registers, where the relatively
clear distinctions in the original Russian are blurred in translation in terms of
morphosyntax.
Now, the question is whether the amount and type of translationese can be
explained by the degree of the cross-linguistic similarity between the registers or
they have to be attributed to the extralinguistic factors such as translational norms
operating in the contemporary professional community and the other translation
process variables such as the input of editors and working conditions. Or in other
166 M. Kunilovskaya and G. Corpas Pastor
words, is translationese a function of the linguistic distance between registers?
From our observations in Fig. 6this not likely to be the case.
The previous research on human translations reports different results in this
respect based on translationese properties induced by different SLs. Diana Santos
observes that languages closeness as a factor in translations has a paradoxical effect:
‘the closer the languages the larger the quantity of false friends and cognates, both
in lexicon and in grammar’, because it is easier to carry over the SL properties
(Santos 1995: 64). Sominsky and Wintner concluded that ‘translationese is more
pronounced, and interference is more powerful, when the two languages are more
distant’based on their classification result in the SL detection task (Sominsky and
Wintner 2019: 1138).
An apparent reconciliation for these competing observations is found in
(Nikolaev et al. 2020). They explore the predictability of translations and find
differences between translations from structurally similar and structurally dissimilar
source languages. In the former case translations tend to employ an intersection of
syntactic patterns found in both languages, which makes them less rich, more
repetitive, in the latter case ‘translators find it hard to fully rework the original
morphosyntactic patterns and produce unpredictable/entropic non-idiomatic trans-
lations’(Nikolaev et al. 2020).
In our setting this should be observed as the difference for the degrees of
homogeneity of the respective translated corpora: the more cross-linguistically
similar registers (fiction and news commentary) should demonstrate higher degree
of homogeneity in translation. This was indeed observed in our data where the
averaged vector distance to centroid was 3.050 and 2.488 for fiction and news
commentary. For more distant registers―media and popular science―this measure
returned 3.354 and 3.281. Note that for distances the smaller numbers mean more
similar texts.
6 Conclusion
In this chapter we investigated the impact of register on the properties of transla-
tions in the English-Russian language pair. We used parallel corpora of professional
translations and comparable reference corpora from the national corpora in four
registers (general media, popular science, fiction, news commentary) to explore the
relations of the original texts in the two languages and the translated registers. Our
approach exploits linguistically interpretable features and is contingent on their
selection and effectiveness for capturing differences between registers, on the one
hand, and translationally relevant text types (sources, targets, and TL reference), on
the other. For both tasks we tested and described the behaviour of 45 mor-
phosyntactic and 11 lexical features. The former represent the text structure in terms
of general text properties, frequencies of PoS and syntactic phenomena, the latter
provide text characteristics from the point of view of lexical predictability scores
and the ratios of high-frequency and low-frequency n-grams.
Translationese and Register Variation …167
The results demonstrate that our experimental setup, including the suggested
features, is reliable for distinguishing registers in translated and non-translated
language as well as for predicting translations in each register, and, therefore, can
be used for revealing the register-related specificity of translations in the given
language pair. Admittedly, the features used are language pair specific, and out
findings apply for English-to-Russian translation. We leave testing the suggested
methodology on other language pairs for future work.
Our findings contribute to the understanding of the linguistic properties of
Russian translations from English in general and to the investigation of their
specificity across registers. We suggested a distance-based method to estimate the
general shapes of translationese in a register-balanced corpus for comparative
analysis, taking into account the cross-linguistic properties of each register. A novel
bottom-up approach was used to associate the linguistic features with a number of
translationese effects and to disentangle the opposite translational tendencies.
We demonstrated that (1) professional translations in all registers are easily
distinguishable from non-translations and these distinctions mostly involve mor-
phosyntactic, rather than lexical, properties; (2) more than a third of all transla-
tionese indicators have their frequencies shifted towards the values observed in the
SL (shining-through features), but their actual impact on the classification results
varies and can be overshadowed by strong features representing other trends;
(3) each register generates a unique form of translationese, with the various
translationese effects contributing to a different extent and being realised through
widely diverging sets of features; (4) translated registers have more regularity in
feature frequencies and higher intra-category homogeneity than their non-translated
English and Russian counterparts. The more cross-linguistically similar registers
seem to generate the more homogeneous translations.
One important message from this research is that human translations vary
depending on the register. Some of this variation can be explained linguistically.
However, some of the translation strategies are likely to be dictated by the estab-
lished practice and professional norms operating in each register, including the
tolerance to translationese.
The scope of this work did not allow us to perform in-depth analysis of the
individual features that were identified as having translationally interesting beha-
viours. The machine learning results can be convincing mathematically, but they
remain a noumenon unless they are related to human perception.
Although this research takes into account the specificity of the given language
pair, it would certainly be interesting to extend it to other target languages or
language pairs. The more immediate development would be to consider other
registers in the explored language pair, if the necessary corpus resources are
available. We hope that this research will promote the idea that register is one of the
central factors in translationese studies, even if its impact on the translation prop-
erties is not defined by purely linguistic matters.
168 M. Kunilovskaya and G. Corpas Pastor
Acknowledgements The research presented in this paper has been partially carried out in the
framework of projects in the framework of the projects VIP (FFI2016-75831-P), TRIAGE
(UMA18-FEDERJA-067) and MI4ALL (CEI-RIS3). The authors would like to thank two
anonymous reviewers for their valuable comments.
Appendix
The UD-based and list-based features in alphabetical order.
Preliminary Notes
1. Normalisation measures
We use several norms to make features comparable across different-size corpora,
depending on the nature of the feature. Most of the features, including all types of
discourse markers, negative particles, passives, types of verb forms, relative clau-
ses, correlative constructions, adverbial clauses introduced by pronominal adverbs
coordinating and subordinating conjunctions, simple sentences, number of clauses
per sentence, are normalised to the number of sentences (30 features). Such features
as personal, possessive and other noun substitutes, nouns, adverbial quantifiers,
determiners are normalised to the running words (6 features). Counts for syntactic
relations are represented as probabilities, normalised to the number of sentences (7
features). Some features have their own normalisation basis: comparative and
superlative degrees are normalised to the total number of adjectives and adverbs,
nouns in the functions of subject, object or indirect object are normalised to the total
number of these roles in the text.
2. Groups of discourse markers
The classification of connectives (discourse markers) follows the descriptions in
Halliday and Hasan (1976) and in Biber et al. (1999). Table A has the number of
items in each group and most frequent examples. The lists were initially produced
independently from grammar reference books, dictionaries of function words and
relevant research papers (for English we used Biber et al. (1999), Fraser (2006), Liu
(2008); for Russian―Novikova (2008), Priyatkina (2015), Russian Grammar
(Shvedova 1980) to name just a few sources for each language). After the initial
selection, the lists were verified for comparability. Following Fraser (2006), dis-
course markers are treated functionally and include items of various morphological
and structural types (conjunctions, adverbs, particles, parenthetical phrases).
Though most items on the lists are set phrases, we allowed for possible lexical and
structural variability at the extraction time. We also used orthography and punc-
tuation to disambiguate our items. The output of the extraction procedure was
manually checked to exclude greedy matching.
Translationese and Register Variation …169
3. The alphabetic list of 45 morphosyntactic features
acl
finite and non-finite clausal modifier of noun (adjectival clause), including relative
clauses as a subtype (used only in EN and RU); extraction is based on UD default
annotation (e.g. the person showing (acl) her around; help people do something to
overcome (acl) it; людeй,cлeдящиx (acl) зaпoлитикoй)
addit
additive connectives; cumulative frequency of the list items normalised to the
number of sentences; see description in Table A
advers
adversative (contrastive) connectives; cumulative frequency of the list items nor-
malised to the number of sentences; see description in Table A
Table 8 Number of listed connectives and discourse markers by category for each of the project
languages and top five most frequent items
English Russian
Additive 52 52
Also, such as, for example, not only,
for instance, in particular, moreover,
in other words, namely
Taкжe, пpиэтoм,нaпpимep, кpoмe
тoгo, вчacтнocти,ктoмyжe, нa
caмoмдeлe, a имeннo, иными
cлoвaми,тoчнee, пpичeм,вдoбaвoк
Adversative 46 34
Still, however, rather than, instead,
though, on the other hand, in fact,
despite
Oднaкo, xoтя,впpoчeм,пpaвдa,
нecмoтpянa, вoтличиeoт,вмecтec
тeм,вcё-тaки,нoнacaмoмдeлe,
нaoбopoт,нaпpoтив,зaтo
Causative 42 49
Because, so, due to, so that,
therefore, as a result, after all, for this
reason, consequently
Пoтoмy, пoэтoмy, пocкoлькy, вeдь,
тaк,, вpeзyльтaтe, paди тoгo,
чтoбы,зaтeм,чтo, пoлyчaeтcя,в
этoмcлyчae, вcвязи cтeм,дaбы,
тeмбoлee чтo
Temporal
and
sequential
110 48
While, since, soon, and then,
eventually, further, anyway, thus, at
the same time, ultimately, meanwhile
Пoкa, нaкoнeц,зaтeм,вцeлoм,втo
вpeмя,кaк,взaключeниe, вкoнцe
кoнцoв,вo-пepвыx, втoжeвpeмя
Epistemic
markers
64 86
Really, at least, perhaps, of course,
probably, in any case, for sure, in
reality, no doubt, arguably, clearly,
indeed, I/we think, I/we am/are (un)
convinced/sure
Кoнeчнo, вoзмoжнo, мoжeт быть,
дeйcтвитeльнo, гoвopят,нaмoй
взгляд,якoбы,пoлaгaю,пocyти,в
любoмcлyчae, кaжeтcя,бeccпopнo,
пoжaлyй
170 M. Kunilovskaya and G. Corpas Pastor
attrib
adjectives and participles functioning as attributes; all words tagged as ADJ or
VerbForm = Part with the amod dependency to their head (e.g. the rising sun; the
coloured face; fried green tomatoes)
aux
auxiliary verbs; extraction is based on UD default annotation
aux:pass
auxiliary verbs in passive forms; extraction is based on UD default annotation
but
contrastive coordinating conjunction but (нo), if not followed but also/и,тaкжe
and not in the absolute sentence end
caus
causative connectives; cumulative frequency of the list items normalised to the
number of sentences; see description in Table A
ccomp
clausal complement as annotated in UD (e.g. help people to do (ccomp) smth; нe
oжидaли,чтoпpидeт(ccomp))
cconj
coordinating conjunctions: lemmas in and, or, both, yet, either, &, nor, plus, nei-
ther, ether / и,a,или,ни,дa, пpичeм,либo, зaтo, инaчe, тoлькo, aн,и/или,иль
tagged CCONJ. Lists are used to filter out noise.
comp
comparative degree of comparison for adjectives and adverbs; synthetic forms are
extracted based on the tag Degree = Comp, while analytical forms are counted as
adjectives and adverbs with a dependent more/бoлee (бoльший)
copula
copula verbs; lemmas of be, быть,этothat have a cop relation to their head,
excluding constructions with there as head for English
correl
correlative constructions of all types, where a PRON/DET (those, such) is syn-
tactically or semantically connected to subsequent CONJ. In English they make a
subset of relative clauses; in Russian they can also be a subtype of a clausal
complement (e.g. of those who voted for him, raising the living standards of those
that are poor)
Translationese and Register Variation …171
demdets
pronominal determiners; lemmas in the function det from the lists this, some, these,
that, any, all, every, another, each, those, either, such /этoт,вecь,тoт,тaкoй,
кaкoй,кaждый,любoй,нeкoтopый,кaкoй-тo, oдин,ceй,этo, вcякий,нeкий,
кaкoй-либo, кaкoй-нибyдь,кoe-кaкoй
deverbals
deverbal nouns, names of processes, actions, states. The extraction for English
accounts for affixation (with most productive -ment, -tion/ -ung, -tion) and con-
version as types of derivation. In the first case the output is filtered with an
empirically driven stop list. Converted nouns are counted from a list of true pro-
cedural nouns that were not fully substantivised. To produce this list we looked
through the nounal occurrences of lemmas that also appear as verbs and filtered out
items that prevail in their fully substantivised lexico-semantic variants in our data
(such as design, set, measure, mark, press, stick, cross, trap, handle). For Russian
we extracted nouns in -тиe, -eниe, -aниe, -cтвo, -ция,-oтaand employed a
150-items long stop list to exclude fully substantivised words such as coбpaниe,
мecтopoждeниe, миниcтepcтвo, тeлeвидeниe, твopчecтвo, peшeниe.
epist
epistemic stance discourse markers; cumulative frequency of the list items nor-
malised to the number of sentences; see description in Table A
finites
verbs in finite form; extraction is based on UD default annotation VerbForm = Fin
indef
noun substitutes, i.e. pronouns par excellence, of indefinite, total and negative
semantic subtypes; extraction is based on PRON tag with a filter list: anybody,
anyone, anything, everybody, everyone, everything, nobody, none, nothing, some-
body, someone, something, elsewhere, nowhere, everywhere, somewhere, anywhere
/кoгдa, гдe, кyдa, oткyдa, oтчeгo, пoчeмy, зaчeмand words with -тo|-нибyдь|-
либo, except starting with кaкoй; and items from ктo-ктo, кoгo-кoгo, кoмy-кoмy,
кeм-кeм,кoм-кoм,чтo-чтo, чeгo-чeгo, чeмy-чeмy, чeм-чeм,кyдa-кyдa, гдe-гдe
infs
infinitives: all cases of a verb form tagged VerbForm = Inf with a dependent to
particle and cases of true bare infinitive, excluding after modal verbs and have to,
going to and modal adjectival predicates, but including cases after help, make, bid,
let, see, hear, watch, dare, feel. For Russian all occurrences of verb forms with the
feature VerbForm = Inf except after modal predicates and with the dependent
быть to exclude future forms (e.g. oтнoшeния бyдyтyxyдшaтьcя).
interrog
interrogative sentences: all sentences ending in ?
lexdens
172 M. Kunilovskaya and G. Corpas Pastor
lexical density: ratio of PoS disambiguated content words types (look_VERB vs
look_NOUN) to all tokens
lexTTR
lexical type-to-token ratio: ratio of PoS disambiguated content words types
(look_VERB vs look_NOUN) to their tokens. Content words include lemmas in
ADJ, ADV, VERB, NOUN part-of-speech categories.
mdd
mean dependency distance (MDD, aka comprehension difficulty) as ‘the distance
between words and their parents, measured in terms of intervening words’(Jing and
Liu 2015: 162)
mhd
mean hierarchical distance (MHD, aka production (speaker’s difficulty) as the
average value of all path lengths travelling from the root to all nodes along the
dependency edges (Jing and Liu 2015: 164)
mpred
modal predicates; for English all verbs tagged as MD in XPOS, except will/shall,
constructions with have-to-Inf and all adjectival modal predicates (given a list of 17
predicatives such as impossible, likely, sure with a dependent AUX). For Russian:
lemma мoчь, lemma cлeдoвaть with a dependent infinitive, three modal adverbs
(мoжнo, нeльзя,нaдo) and 11 adjectives from the modal predicative list in the
short form Variant = Short (e.g. дoлжeн,cпocoбный,вoзмoжный)
mquantif
adverbial quantifiers; listed lemmas tagged ADV. The support lists include 37
English items (e.g. barely, completely, intensely, almost), 80 Russian items
(aбcoлютнo, пoлнocтью,c
плoшь,нeoбыкнoвeннo, дocтaтoчнo, coвepшeннo,
нeвынocимo, пpимepнo). For Russian we additionally provide for functionally
similar non-adverbial quantifiers such as eлe, oчeнь,вшecтepo, нeвыpaзимo,
излишнe, eлe-eлe, чyть-чyть,eдвa-eдвa, тoлькo, кaпeлькy, чyтoчкy, eдвa.
neg
negative particles or main sentence negation: counts of lemmas in no, not, neither /
нeт,нe
nnargs
core verbal arguments represented by nouns or proper names; ratio of nouns and
proper names in the functions of nsubj, obj, iobj to the count of these functions
nsubj:pass
subjects of verbs in the passive voice; extraction is based on UD default nsubj:pass
annotation
Translationese and Register Variation …173
numcls
number of clauses per sentence; number of relations from the list csubj, acl:relcl,
advcl, acl, xcomp, parataxis annotated in one sentence
passives
passive constructions with expressed agentive role; all verbs tagged Voice = Pass
and a dependent aux:pass (for English). For Russian we account for two mor-
phological forms (вoйнaвeлacь,пoлитикaбылaнaпpaвлeнa) and for semantic
passive (cтaдиoнвoзвoдят нaнoвoммecтe, вoBлaдикaвкaзeeмyгoтoвят
paдyшнyювcтpeчy)
parataxis
asyndatically connected coordinated clauses (often direct speech or clauses joined
‘:’or a ‘;’as well as parenthetical clauses); extraction is based on UD default
annotation
pasttense
verbs in the past tense: all occurrences of the feature Tense = Past
pied
correlative constructions with displaced (pied-piped) preposition (e.g. technology
for which Sony could take credit; speech in which he made this argument; o
тaкoм,oкaкoмвынecлыxaли;cкaндaл,вкoтopoм;тpaгeдии,cкoтopыми,в
тoйкoнcтpyкции,вкaкoйoнa)
possdet
possessive pronouns; for English lemma in my, your, his, her, its, our, their tagged
DET, PRON and Poss = Yes. For Russian lemma in мoй,твoй,вaш,eгo, ee, eё,
нaш,иx, иxний,cвoйtagged DET
ppron
personal pronouns; tokens tagged PRON, with any value of attribute Person = that
do not have Poss = Yes feature and are on the list: i, you, he, she, it, we, they, me,
him, her, us, them /я,ты,вы,oн,oнa, oнo, мы,oни,мeня,тeбя,eгo, eё, ee, нac,
вac, иx, нeё,нee, нeгo, ниx, мнe, тeбe, eй,eмy, нaм,вaм,им,нeй,нeмy, ним,
мeня,тeбя,нeгo, мнoй,мнoю,тoбoй,тoбoю,Baми,им,eй,eю,нaми,вaми,
ими,ним,нeм,нём,нeй,нeю
pverbals
participles: for English all occurrences of VerbForm = Part or VerbForm = Ger not
in attributive function amod or part of an analytical form. For Russian
VerbForm = Part not in the short form and not in the attributive function, without a
dependent auxiliary, and VerbForm = Conv without dependent auxiliary (e.g. after
years of translating emails, webinars and other materials)
174 M. Kunilovskaya and G. Corpas Pastor
relativ
all relative clauses, including correlative constructions and pied-piping construc-
tion. Extraction is based on affirmative sentences only. For English: which, that,
whose, whom, what, who tagged as PRON, excluding cases when relative PRON
has a dependent preposition and follows its head (e.g. But we will return to that
(PRON) later). For Russian: кoтopый,чтo, ктo, кaкoйand a comma in the left
window of 3
sconj
subordinating conjunctions: lemma in that, if, as, of, while, because, by, for, to,
than, whether, in, about, before, after, on, with, from, like, although, though, since,
once, so, at, without, until, into, despite, unless, whereas, over, upon, whilst,
beyond, towards, toward, but, except, cause, together / чтo, кaк,ecли,чтoбы,тo,
кoгдa, чeм,xoтя,пocкoлькy, пoкa, тeм,вeдь,нeжeли,ибo, пycть,бyдтo,
cлoвнo, дaбы,paз,нacкoлькo, тoт,кoли,кoль,xoть,paзвe, cкoль,eжeли,
пoкyдa, пocтoлькytagged SCONJ. Lists are used to filter out noise.
sentlength
number of words per sentence averaged over all sentences in the text. The
extraction accounts for typical sentence tokenisation errors such as sentences
ending in:,;, Mr., Dr.
simple
simple sentence; a sentence where no words have relations: csubj, acl:relcl, advcl,
acl, xcomp, parataxis
sup
superlative degree of comparison for adjective and adverbs; synthetic forms are
extracted based on the tag Degree = Sup, while analytical forms are counted as
adjectives and adverbs with a dependent most/нaибoлee/caмый and for Russian
words starting with нaи-with the exception of a few homonymous adverbs
(нaиcкocoк)
tempseq
temporal and sequential connectives; cumulative frequency of the list items nor-
malised to the number of sentences; see description in Table A
whconj
adverbial clause introduced by a pronominal ADV when, where, why / кoгдa, гдe,
кyдa, oткyдa, oтчeгo, пoчeмy, зaчeм
xcomp
a predicative or clausal complement without its own subject, annotated after phrasal
verbs (e.g. started to sing), in case of infinitive constructions (e.g. asked me to
leave), etc.; extraction is based on UD default annotation
Translationese and Register Variation …175
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Maria Kunilovskaya PhD in contrastive linguistics, has an extensive experience in translator
education and corpus linguistics. Her research is recently focused on human translation quality
estimation, translationese studies and varieties, building and exploiting comparable and parallel
corpora. Maria’s other interests include computational and empirical approaches to comparing
languages and understanding translation processes.
Gloria Corpas Pastor PhD in English philology, Professor in translation, interpreting and
translation technology and an active member of several international and national editorial and
scientific committees. She is the head of the LEXYTRAD research group. Her research interests
include specialised translation and interpreting technologies, corpus-based translation studies,
phraseology, lexicography and terminology.
180 M. Kunilovskaya and G. Corpas Pastor
