Authors:
Yue JiangSchool of Foreign Studies, Xi'an Jiaotong University, Xi'an, PR China

Search for other papers by Yue Jiang in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-0310-2657
and
Jiang NiuSchool of Foreign Studies, Xi'an Jiaotong University, Xi'an, PR China

Search for other papers by Jiang Niu in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-4980-1936
Restricted access

Abstract

Earlier studies have corroborated that human translation exhibits unique linguistic features, usually referred to as translationese. However, research on machine translationese, in spite of some sparse efforts, is still in its infancy. By comparing machine translation with human translation and original target language texts, this study aims to investigate if machine translation has unique linguistic features of its own too, to what extent machine translations are different from human translations and target-language originals, and what characteristics are typical of machine translations. To this end, we collected a corpus containing English translations of modern Chinese literary texts produced by neural machine translation systems and human professional translators and comparable original texts in the target language. Based on the corpus, a quantitative study of discourse coherence was conducted by observing metrics in three dimensions borrowed from Coh-Metrix, including connectives, latent semantic analysis and the situation/mental model. The results support the existence of translationese in both human and machine translations when they are compared with original texts. However, machine translationese is not the same as human translationese in some metrics of discourse coherence. Additionally, machine translation systems, such as Google and DeepL, when compared with each other, show unique features in some coherence metrics, although on the whole they are not significantly different from each other in those coherence metrics.

  • Aranberri, N. (2020). Can translationese features help users select an MT system for post-editing? Procesamiento del Lenguaje Natural, 64, 93100.

    • Search Google Scholar
    • Export Citation
  • Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.

  • Baker, M. (1993). Corpus linguistics and translation studies: Implications and applications. In M. Baker, G. Francis, & E. Tognini-Bonelli (Eds.), Text and technology: In honour of John Sinclair (pp. 233250). John Benjamins.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Becher, V. (2011). When and why do translators add connectives?: A corpus-based study. Target, 23(1), 2647.

  • Bizzoni, Y., Juzek, T. S., España-Bonet, C., Chowdhury, K. D., van Genabith, J., & Teich, E. (2020). How human is machine translationese? Comparing human and machinetranslations of text and speech. Proceedings of the 17th International conference on spoken language translation (pp. 280290). Association for Computational Linguistics.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blum-Kulka, S. (1986). Shifts of ccohesion and coherence in translation. In J. House, & S. Blum-Kulka (Eds.), Interlingual and intercultural communication: Discourse and cognition in translation and second language acquisition studies (pp. 1735). Tübingen: Narr.

    • Search Google Scholar
    • Export Citation
  • Cadwell, P., O’Brien, S., & Teixeira, C. S. (2018). Resistance and accommodation: Factors for the (non-)adoption of achine translation among professional translators. Perspectives, 26(3), 301321.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J. W. (2006). Explicitation through the Use of Connectives in translated Chinese: A corpus-based study. [Doctoral dissertation, University of Manchester]. e-theses online service of University of Manchester. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.521458.

    • Search Google Scholar
    • Export Citation
  • Cohen, J. (1969). Statistical power analysis for the behavioral sciences. Academic Press.

  • Čulo, O. (2014). Approaching machine translation from translation studies–a perspective on commonalities, potentials, differences. Proceedings of the 17th annual conference of the European association for machine translation (pp. 199206). The European Association for Machine Translation.

    • Search Google Scholar
    • Export Citation
  • Čulo, O., & Nitzke, J. (2016). Patterns of terminological variation in post-editing and of cognate use in machine translation in contrast to human translation. Proceedings of the 19th annual conference of the European association for machine translation (pp. 106114).

    • Search Google Scholar
    • Export Citation
  • Foltz, P. W., Kintsch, W., & Landauer, T. K. (1998). The measurement of textual coherence with latent semantic analysis. Discourse Processes, 25(2–3), 285307.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frawley, W. (1984). Prolegomenon to a theory of translation. In W. Frawley (Ed), Translation: Literary, linguistic, and philosophical perspectives (pp. 159175). Associated University Presses.

    • Search Google Scholar
    • Export Citation
  • Gellerstam, M. (1986). Translationese in Swedish novels translated from English. Translation Studies in Scandinavia, 1, 8895.

  • Graesser, A. C., & McNamara, D. S. (2011). Computational analyses of multilevel discourse comprehension. Topics in Cognitive Science, 3(2), 371398.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graesser, A. C., McNamara, D. S., Louwerse, M. M., & Cai, Z. (2004). Coh-metrix: Analysis of text on cohesion and language. Behavioral Research Methods, Instruments, & Computers, 36, 193202. https://doi.org/10.3758/BF03195564.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graham, Y., Haddow, B., & Koehn, P. (2019). Translationese in machine translation evaluation. arXiv:1906.09833.

  • Granger, S. (2017). Tracking the third code: A cross-linguistic corpus-driven approach to metadiscursive markers. In A. Čermáková, & M. Mahlberg (Eds.), The corpus linguistics discourse: In honour of Wolfgang Teubert (pp. 185204). John Benjamins.

    • Search Google Scholar
    • Export Citation
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2014). Multivariate data analysis. Pearson Education.

  • Halliday, M. A. K., & Hasan, R. (1976). Cohesion in English. Routledge.

  • Hassan, H., Aue, A., Chen, C., Chowdhary, V., Clark, J., Federmann, C., Huang, X., Junczys-Dowmunt, M., Lewis, W., Li, M., Liu, S., Liu, T., Luo, R., Menezes, A., Qin, T., Seide, F., Tan, X., Tian, F., Wu, L., & Zhou, M. (2018). Achieving human parity on automatic Chinese to English news translation. arXiv:1803.05567v2.

    • Search Google Scholar
    • Export Citation
  • Kajzer-Wietrzny, M. (2022). An intermodal approach to cohesion in constrained and unconstrained language. Target, 34(1), 130162.

  • Károly, K. (2010). Shifts in repetition vs. shifts in text meaning: A study of the textual role of lexical repetition in non-literary translation. Target, 22(1), 4070.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Károly, K. (2014). Referential cohesion and news content: A case study of shifts of reference in Hungarian-English news translation. Target, 26(3), 406431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kintsch, W. (1998). Comprehension: A paradigm for cognition. Cambridge University Press.

  • Koponen, M. (2016). Is machine translation post-editing worth the effort? A survey of research into post-editing and effort. The Journal of Specialised Translation, 25, 131148.

    • Search Google Scholar
    • Export Citation
  • Kruger, H. (2012). A corpus-based study of the mediation effect in translated and edited language. Target, 24(2), 355388.

  • Kruger, H. (2018). That again: A multivariate analysis of the factors conditioning syntactic explicitness in translated English. Across Languages and Cultures, 20(1), 133.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krüger, R. (2020a). Explicitation in neural machine translation. Across Languages and Cultures, 21(2), 195216.

  • Krüger, R. (2020b). Propositional opaqueness as a potential problem for neural machine translation. In B. Ahrens, M. Beaton-Thome, M. Krein-Kühle, R. Krüger, L. Link, & U. Wienen (Eds.), Interdependence and innovation in translation, interpreting and specialised communication (pp. 261278). Frank & Timme.

    • Search Google Scholar
    • Export Citation
  • Kuo, C. L. (2019). Function words in statistical machine-translated Chinese and original Chinese: A study into the translationese of machine translation systems. Digital Scholarship in the Humanities, 34(4), 752771.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch, W. (Eds.), (2007). Handbook of latent semantic analysis. Erlbaum.

  • Lapshinova-Koltunski, E. (2015). Variation in translation: Evidence from corpora. In C. Fantinuoli, & F. Zanettin (Eds.), New directions in corpus-based translation studies (pp. 8199). Langugae Science Press.

    • Search Google Scholar
    • Export Citation
  • Läubli, S., Sennrich, R., & Volk, M. (2018). Has machine translation achieved human parity? A case for document-level evaluation. arXiv:1808.07048v1.

    • Search Google Scholar
    • Export Citation
  • Loock, R. (2020). No more rage against the machine: How the corpus-based identification of machine-translationese can lead to student empowerment. The Journal of Specialised Translation, 34, 150170.

    • Search Google Scholar
    • Export Citation
  • Louwerse, M. (2001). An analytic and cognitive parameterization of coherence relations. Cognitive Linguistics, 12, 291315.

  • Macken, L., Prou, D., & Tezcan, A. (2020). Quantifying the effect of machine translation in a high-quality human translation production process. Informatics, 7(2), 119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mauranen, A. (2000). Strange strings in translated language: A study on corpora. In M. Olohan (Ed.), Intercultural faultlines: Research models intranslation studies (pp. 119142). Routledge.

    • Search Google Scholar
    • Export Citation
  • McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge University Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990). Introduction to WordNet: An on-line lexical database. International Journal of Lexicography, 3(4), 235244.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moorkens, J. (2017). Under pressure: Translation in times of austerity. Perspectives, 25(3), 464477.

  • Niu, J., Jiang, Y., & Zhou, Y. (2020). Approaching textual coherence of machine translation with complex network. International Journal of Modern Physics C, 31(12), 121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Brien, S. (2012). Translation as human-computer interaction. Translation Spaces, 1(1), 101122.

  • Olohan, M., & Baker, M. (2000). Reporting that in translated English. Evidence for subconscious processes of explicitation? Across Languages and Cultures, 1(2), 141158.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Öner Bulut, S. (2019). Integrating machine translation into translator training: Towards ‘Human Translator Competence. transLogos, 2(2), 126.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Øverås, L. (1998). In search of the third code: An investigation of norms in literary translation. Meta, 43(4), 557570.

  • Puurtinen, T. (2003). Genre-specific features of translationese? Linguistic differences between translated and non-translated Finnish children's literature. Literary and Linguistic Computing, 18(4), 389406.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rohdenburg, G. (1996). Cognitive complexity and increased grammatical explicitness in English. Cognitive Linguistics, 7(2), 149182.

  • Rossi, C., & Chevrot, J. P. (2019). Uses and perceptions of machine translation at the European Commission. The Journal of Specialised Translation, 31, 177200.

    • Search Google Scholar
    • Export Citation
  • Tirkkonen-Condit, S. (2002). Translationese—a myth or an empirical fact?: A study into the linguistic identifiability of translated language. Target, 14(2), 207220.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toral, A., Castilho, S., Hu, K., & Way, A. (2018). Attaining the unattainable? Reassessing claims of human parity in neural machine translation (p. 10432). arXiv:1808.

    • Search Google Scholar
    • Export Citation
  • Vanmassenhove, E., Shterionov, D., & Gwilliam, M. (2021). Machine translationese: Effects of algorithmic bias on linguistic complexity in machine translation. arXiv:2102.00287.

    • Search Google Scholar
    • Export Citation
  • Vanmassenhove, E., Shterionov, D., & Way, A. (2019). Lost in translation: Loss and decay of linguistic richness in machine translation. arXiv:1906.12068.

    • Search Google Scholar
    • Export Citation
  • Way, A. (2018). Quality expectations of machine translation. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation quality assessment (pp. 159178). Springer.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wintner, S. (2016). Translationese: Between human and machine translation. Proceedings of COLING 2016, the 26th International conference on computational linguistics: Tutorial abstracts (pp. 1819). Association for Computational Linguistics.

    • Search Google Scholar
    • Export Citation
  • Wong, B. T., & Kit, C. (2012). Extending machine translation evaluation metrics with lexical cohesion to document level. Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning (pp. 10601068). Association for Computational Linguistics.

    • Search Google Scholar
    • Export Citation
  • Xiao, R. (2011). Word clusters and reformulation markers in Chinese and English: Implications for translation universal hypotheses. Languages in Contrast, 11(2), 145171.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, J. (2019). Yuliaoku yu huayu yanjiu [Corpora and Discourse Studies]. Beijing: Foreign Language Teaching and Research Press.

  • Zhang, B., Zhu, J., & Su, H. (2020). Maixiang disandai rengongzhinneg [Toward the third generation of artifificial intelligence]. SCIENTIA SINICA: Informationis, 50(9), 12811302.

    • Search Google Scholar
    • Export Citation
  • Zufferey, S., & Cartoni, B. (2014). A multifactorial analysis of explicitation in translation. Target, 26(3), 361384.

  • Zwaan, R. A., & Radvansky, G. A. (1998). Situation models in language comprehension and memory. Psychological Bulletin, 123(2), 162185.

  • Collapse
  • Expand

 

Author Guidelines are available in PDF format.
Please, download the file from HERE.

 

Editor-in-Chief: Kinga KLAUDY (Eötvös Loránd University, Hungary)

Consulting Editor: Pál HELTAI (Kodolányi János University, Hungary)

Managing Editor: Krisztina KÁROLY (Eötvös Loránd University, Hungary)

EDITORIAL BOARD

  • Andrew CHESTERMAN (University of Helsinki, Finland)
  • Kirsten MALMKJÆR (University of Leicester, UK)
  • Christiane NORD (University of Free State, Bloemfontein, South Africa)
  • Anthony PYM (Universitat Rovira i Virgili, Tarragona, Spain, University of Melbourne, Australia)
  • Mary SNELL-HORNBY (University of Vienna, Austria)
  • Sonja TIRKKONEN-CONDIT (University of Eastern Finland, Joensuu, Finland)

ADVISORY BOARD

  • Mona BAKER (Shanghai International Studies University, China, University of Oslo, Norway)
  • Łucja BIEL (University of Warsaw, Poland)
  • Gloria CORPAS PASTOR (University of Malaga, Spain; University of Wolverhampton, UK)
  • Rodica DIMITRIU (Universitatea „Alexandru Ioan Cuza” Iasi, Romania)
  • Birgitta Englund DIMITROVA (Stockholm University, Sweden)
  • Sylvia KALINA (Cologne Technical University, Germany)
  • Haidee KOTZE (Utrecht University, The Netherlands)
  • Sara LAVIOSA (Università degli Studi di Bari Aldo Moro, Italy)
  • Brian MOSSOP (York University, Toronto, Canada)
  • Orero PILAR (Universidad Autónoma de Barcelona, Spain)
  • Gábor PRÓSZÉKY (Hungarian Research Institute for Linguistics, Hungary)
  • Alessandra RICCARDI (University of Trieste, Italy)
  • Edina ROBIN (Eötvös Loránd University, Hungary)
  • Myriam SALAMA-CARR (University of Manchester, UK)
  • Mohammad Saleh SANATIFAR (independent researcher, Iran)
  • Sanjun SUN (Beijing Foreign Studies University, China)
  • Anikó SOHÁR (Pázmány Péter Catholic University,  Hungary)
  • Sonia VANDEPITTE (University of Gent, Belgium)
  • Albert VERMES (Eszterházy Károly University, Hungary)
  • Yifan ZHU (Shanghai Jiao Tong Univeristy, China)

Prof. Kinga Klaudy
Eötvös Loránd University, Department of Translation and Interpreting
Múzeum krt. 4. Bldg. F, I/9-11, H-1088 Budapest, Hungary
Phone: (+36 1) 411 6500/5894
Fax: (+36 1) 485 5217
E-mail: 

  • WoS Arts & Humanities Citation Index
  • Wos Social Sciences Citation Index
  • WoS Essential Science Indicators
  • Scopus
  • Linguistics Abstracts
  • Linguistics and Language Behaviour Abstracts
  • Translation Studies Abstracts

2021  
Web of Science  
Total Cites
WoS
214
Journal Impact Factor 1,292
Rank by Impact Factor Linguistics 98/194
Impact Factor
without
Journal Self Cites
1,208
5 Year
Impact Factor
1,210
Journal Citation Indicator 0,85
Rank by Journal Citation Indicator Language & Linguistics 108/370
Linguistics 122/274
Scimago  
Scimago
H-index
19
Scimago
Journal Rank
0,994
Scimago Quartile Score Linguistics and Language 67/1103 (Q1)
Scopus  
Scopus
Cite Score
2,5
Scopus
CIte Score Rank
Language and Linguistics 121/968 (Q1, D2)
Linguistics and Language 128/1032 (Q1, D2)
Scopus
SNIP
1,576

2020  
Total Cites
WoS
169
Journal Impact Factor 1,160
Rank by Impact Factor

Linguistics 99/193 (Q3)
Languages & Linguistics 57/205 (Q2)

Impact Factor
without
Journal Self Cites
1,040
5 Year
Impact Factor
1,095
Journal Citation Indicator 1,01
Rank by Journal Citation Indicator

Linguistics 107/259 (Q2)
Language & Linguistics 94/356 (Q2)

Citable
Items
12
Total
Articles
12
Total
Reviews
0
Scimago
H-index
14
Scimago
Journal Rank
1,257
Scimago Quartile Score

Language and Linguistics Q1
Linguistics and Language Q1

Scopus
Cite Score
93/50=1,9

Scopus
Cite Score Rank

Language and Linguistics 130/879 (Q1)
Linguistics and Language 147/935 (Q1)
Scopus
SNIP
1,670

2019  
Total Cites
WoS
91
Impact Factor 0,360
Impact Factor
without
Journal Self Cites
0,320
5 Year
Impact Factor
0,500
Immediacy
Index
0,083
Citable
Items
12
Total
Articles
12
Total
Reviews
0
Cited
Half-Life
n/a
Citing
Half-Life
12,7
Eigenfactor
Score
0,00018
Article Influence
Score
0,234
% Articles
in
Citable Items
100,00
Normalized
Eigenfactor
0,02306
Average
IF
Percentile
20,053 (Q1)
Scimago
H-index
13
Scimago
Journal Rank
0,648
Scopus
Scite Score
94/51=1,8
Scopus
Scite Score Rank
Language and Linguistics 120/830 (Q1)
Linguistics and Language 135/884 (Q1)
Scopus
SNIP
1.357

Across Languages and Cultures
Publication Model Hybrid
Submission Fee

none

Article Processing Charge 900 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2022 Online subsscription: 310 EUR / 384 USD
Print + online subscription: 362 EUR / 452 USD
Subscription fee 2023 Online subsscription: 318 EUR / 384 USD
Print + online subscription: 372 EUR / 452 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles are sold on the displayed price.

Across Languages and Cultures
Language English
Size B5
Year of
Foundation
1999
Volumes
per Year
1
Issues
per Year
2
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1585-1923 (Print)
ISSN 1588-2519 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Jun 2022 0 0 0
Jul 2022 0 0 0
Aug 2022 0 0 0
Sep 2022 0 0 0
Oct 2022 0 0 0
Nov 2022 400 13 23
Dec 2022 17 1 2