Syntactic complexity in translated and non-translated texts: A corpus-based study of simplification

Abstract

This study approaches the investigation of the simplification hypotheses in corpus-based translation studies from a syntactic complexity perspective. The research is based on two comparable corpora, the English monolingual part of COCE (Corpus of Chinese-English) and the native English corpus of FLOB (Freiburg-LOB Corpus of British English). Using the 13 syntactic complexity measures falling into five subconstructs (i.e. length of production unit, amount of subordination, amount of coordination, phrasal complexity and overall sentence complexity), our results show that translation as a whole is less complex compared to non-translation, reflected most prominently in the amount of subordination and overall sentence complexity. Further pairwise comparison of the four subgenres of the corpora shows mixed results. Specifically, the translated news is homogenous to native news as evidenced by the complexity measures; the translated genres of general prose and academic writing are less complex compared to their native counterparts while translated fiction is more complex than non-translated fiction. It was found that mean sentence length always produced a significant effect on syntactic complexity, with higher syntactic complexity for longer sentence lengths in both corpora. ANOVA test shows a highly significant main effect of translation status, with higher syntactic complexity in the non-translated texts (FLOB) than the translated texts (COCE), which provides support for the simplification hypothesis in translation. It is also found that, apart from translation status, genre is an important variable in affecting the complexity level of translated texts. Our study offers new insights into the investigation of simplification hypothesis from the perspective of translation from English into Chinese.

Collocation Use in EFL Learners’ Writing Across Multiple Language Proficiencies: A Corpus-Driven Study

“…Such studies differ remarkedly in the way they define and identify collocation. Taking a statistical perspective of collocation, some have utilized the frequency-based approach, which is frequently adopted in computational linguistics ( Gyllstad, 2007 ; Nguyen and Webb, 2016 ; Liu and Afzaal, 2020 , 2021 ). Studies of such kind are highly quantitative ( Lee and Shin, 2021 ) and based on the notion that collocation pertains to the “probability of occurrence of their constituent words” ( Henriksen, 2013 , p. 31).…”

Entropy-based discrimination between translated Chinese and original Chinese using data mining techniques

“…With its capacity of handling large amount of data, corpus has been used to explore various translation topics and increasingly accepted by translation researchers. CBTS research has helped foster a transition from an overreliance on source texts to a systematic investigation of how translation plays out in the target language system [17]. Following Baker's proposal, a plethora of studies investigating translationese specifically adopted a monolingual comparable corpus consisting of translated texts and non-translated original texts of the same language.…”

“…Such studies are often exploratory in that the frequencies of manually selected features in both translated and non-translated corpora are calculated to show if significant differences exist between the two types of texts. Over the years, researchers have found that translated texts tend to demonstrate a lexically and syntactically simpler [17,18], more explicit [12,19] and more conservative [20] trend than comparable non-translated texts of the same language. In addition to these features, translated texts have been found to underrepresent target language specific elements which do not have equivalents in the source language [21,22] and carry source text language features due to the source language shinningthrough influence [23].…”

“…For example, the examination of translationese or translation universals is often confined to manuallyselected linguistic indicators. The use of such indicators runs the risks of "cherry picking" by deliberately selecting those indicators to confirm the researcher's perceptions or hypothesis [17]. There is clearly a lack of global and holistic features in this line of research.…”

“…However, the wordform entropy cannot measure the syntactic complexity of a text. For such a reason, researchers have instead turned to the use of POS forms as an indicator of syntactic complexity as POS has "attained a certain degree of abstraction for words" [17,27]. Note that this can be linked to the simplification universal that translated texts tend to be simpler than non-translated texts [51,52].…”