1887
Volume 4, Issue 1
  • ISSN 2542-5277
  • E-ISSN: 2542-5285
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Abstract

Abstract

“Literal translation” is a popular construct in Translation Studies. Research from computational approaches has consistently shown that non-literal translations, i.e., renderings semantically and syntactically different or not close to the source text, are more difficult or effortful to produce than literal ones. This paper researches whether literal translations are systematically less effortful to process than non-literal ones using comparable corpus data. The effort incurred in processing literal translations from a parallel corpus is compared to that of processing the most frequent non-literal renderings found in previous comparable corpus studies. Ten professional translators edited a text using a mock translation environment setup using the keylogger Inputlog. The task was presented as a regular editing process with a full cohesive text presented segment pair by segment pair. Time served as a proxy for overall cognitive effort. We analyzed (TTP) and of segment edit (TC), or Results showed that processing efforts are indistinguishable between categories, suggesting that cognitive effort to edit non-literal is not always higher when compared to the most frequent literal translations from a parallel corpus.

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2021-06-07
2025-02-09
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References

  1. Alves, Favio and Tania Liparini Campos
    2009 “Translation Technology In Time: Investigating the impact of translation memory systems and time pressure on types of internal and external support.” InBehind the Mind: Methods, Models and Results in Translation Process Research. Edited byS. Göpferich, A. L. Jakobsen and I. M. Mees. 191–218. Copenhagen: Samfundslitteratur.
    [Google Scholar]
  2. Bundgaard, Kristen and Tina P. Christensen
    2019 “Is the Concordance Feature the New Black? A workplace study of translators’ interaction with translation resources while post-editing TM and MT matches.” Journal of Specialised Translation, 31 (31): 14–37.
    [Google Scholar]
  3. Bürkner, Paul C.
    2017 “brms: An R package for Bayesian multilevel models using Stan.” Journal of Statistical Software80 (1): 1–28. doi:  10.18637/jss.v080.i01
    https://doi.org/10.18637/jss.v080.i01 [Google Scholar]
  4. Carl, Michael, Barbara Dragsted, Jakob Elming, Daniel Hardt and Arnt L. Jakobsen
    2011 “The Process of Post-Editing: A pilot study.” Bernadette Sharp, Michael Zock, Michael Carl, Arnt Lykke Jakobsen. (eds). Proceedings of the 8th International NLPCS Workshop. Special Theme: Human-Machine Interaction in Translation. Copenhagen: Samfundslitteratur, 131–142.
    [Google Scholar]
  5. Carl, Michael and Barbara Dragsted
    2012 “Inside the Monitor Model: Process of default and challenged translation production.” Translation, Computation, Corpora and Cognition2 (1): 127–145.
    [Google Scholar]
  6. Carl, Michael, Silke Gutermuth and Silvia Hansen-Schirra
    2015 “Post-editing Machine Translation: Efficiency, strategies and revision processes in professional translation settings”. InPsycholinguistic and Cognitive Inquiries into Translation and Interpreting. Edited byAline Ferreira and John Schwieter. 145–174. Amsterdam: John Benjamins. 10.1075/btl.115.07car
    https://doi.org/10.1075/btl.115.07car [Google Scholar]
  7. Carl, Michael and Moritz Schaeffer
    2017a “Why Translation is Difficult: A corpus-based study of non-literality in post-editing and from-scratch translation”. Hermes56: 43–57. doi:  10.7146/hjlcb.v0i56.97201
    https://doi.org/10.7146/hjlcb.v0i56.97201 [Google Scholar]
  8. 2017b “Measuring Translation Literality.” InTranslation in Transition. Between Cognition, Computing, and Technology. Edited byArnt L. Jakobsen and Bartolomé Mesa Lao. 81–105. Amsterdam: John Benjamins. 10.1075/btl.133.03car
    https://doi.org/10.1075/btl.133.03car [Google Scholar]
  9. Carl, Michael and Cristina Toledo Báez
    2019 “Machine Translation Errors and the Translation Process: A study across different languages.” Journal of Specialised Translation, (31), 107–132.
    [Google Scholar]
  10. Daems, Joke, Sonia Vandepitte, Robert J. Hartsuiker and Lieve Macken
    2017 “Translation Methods and Experience: A comparative analysis of human translation and post-editing with students and professional translators.” Meta62: 245–270. 10.7202/1041023ar
    https://doi.org/10.7202/1041023ar [Google Scholar]
  11. De Groot, Annette M. B.
    1992 “Determinants of Word Translation.” Journal of Experimental Psychology: Learning, Memory and Cognition18 (5): 1001–1018.
    [Google Scholar]
  12. Dragsted, Barbara
    2010 “Coordination of Reading and Writing Proceses in Translation: An eye on uncharted territory”. InTranslation and Cognition. Edited byGregory M. Shreve and Erik Angelone. 41–62. Amsterdam: John Benjamins. 10.1075/ata.xv.04dra
    https://doi.org/10.1075/ata.xv.04dra [Google Scholar]
  13. Gelman, Andrew, Simpson, Daniel and Michael Betancourt
    2017 “The Prior Can Often Only Be Understood in the Context of the Likelihood.” Entropy19 (10): 1–13. doi:  10.3390/e19100555
    https://doi.org/10.3390/e19100555 [Google Scholar]
  14. Guerberof, Ana
    2009 “Productivity and Quality in the Post-Editing of Outputs from Translation Memories and Machine Translation.” The International Journal of Localisation7 (1): 11–21.
    [Google Scholar]
  15. Hatzidaki, Anna
    2019 “Using Experimental Approaches to Study Translation: The what and how.” Translation, Cognition & Behavior2 (1): 35–54. 10.1075/tcb.00018.hat
    https://doi.org/10.1075/tcb.00018.hat [Google Scholar]
  16. Halverson, Sandra
    2015 “Cognitive Translation Studies and the Merging of Empirical Paradigms: The case of ‘literal translation’.” Translation Spaces4 (2): 310–340. 10.1075/ts.4.2.07hal
    https://doi.org/10.1075/ts.4.2.07hal [Google Scholar]
  17. 2017a “Gravitational Pull in Translation: testing a revised model”. InEmpirical Translation Studies: New Methodological and Theoretical Traditions. Edited byGert De Sutter, Marie-Aude Lefer and Isabelle Delaere. 9–46. Berlin: De Gruyter. 10.1515/9783110459586‑002
    https://doi.org/10.1515/9783110459586-002 [Google Scholar]
  18. 2017b “Multimethods Approaches.” InHandbook of Translation and Cognition. Edited byJohn W. Schwieter and Aline Ferreira. 195–212. Hoboken, NJ: Wiley. 10.1002/9781119241485.ch11
    https://doi.org/10.1002/9781119241485.ch11 [Google Scholar]
  19. 2019 “Default Translation: A construct for Cognitive Translation Studies.” Translation, Cognition & Behavior2 (2): 187–210. 10.1075/tcb.00023.hal
    https://doi.org/10.1075/tcb.00023.hal [Google Scholar]
  20. Heilmann, Arndt and Stella Neumann
    2016 “Dynamic Pause Assessment of Keystroke Logged Data for the Detection of Complexity in transLation and Monolingual Text Production.” InProceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), 98–103. Osaka, Japan: The COLING Organizing Committee.
    [Google Scholar]
  21. Jakobsen, Arnt L.
    1998 “Logging Target Text Production with Translog”. InProbing the Process of Translation: Methods and Results. Edited byGyde Hansen. 9–20. Copenhagen: Samfundslitteratur.
    [Google Scholar]
  22. 2002 “Translation Drafting by Professional Translators and by Translation Students.” InEmpirical translation studies: Process and Product. Edited byGyde Hansen. 191–204. Copenhagen: Samfundslitteratur.
    [Google Scholar]
  23. Jakobsen, Arnt L. and Kristian T. Jensen
    2008 “Eye Movements Behaviour across four Different Types of Reading Task”. Copenhagen Studies in Language36: 103–124.
    [Google Scholar]
  24. Jensen, Kristian Tangsgaard Hvelplund
    2011 “Distribution of Attention Between Source Text and Target Text During Translation”. InCognitive Explorations of Translation. Edited bySharon O’Brien. 215–238. Continuum: London.
    [Google Scholar]
  25. Jia, Yafang, Carl, Michael and Xiangling Wang
    2019 “How Does the Post-Editing of Neural Machine Translation Compare with From-Scratch Translation? A product and process-based study”. Jostrans: The Journal of Specialized Translation31: 60–86.
    [Google Scholar]
  26. Jiménez-Crespo, Miguel A. and María Isabel Tercedor Sánchez
    2017 “Lexical Variation, Register and Explicitation in Medical Translation: A comparable corpus study of medical terminology in US websites translated into Spanish”. TIS: Translation and Interpreting Studies12 (3): 405–426. 10.1075/tis.12.3.03jim
    https://doi.org/10.1075/tis.12.3.03jim [Google Scholar]
  27. . Forthcoming. “Explicitation and Implicitation in Translation: Combining comparable and parallel corpus methodologies.” MONTI, Special Issue CTS Spring-cleaning: A Critical Reflexion.
    [Google Scholar]
  28. Kruschke, John K.
    2018 “Rejecting or Accepting Parameter Values in Bayesian Estimation.” Advances in Methods and Practices in Psychological Science Science1 (2): 270–280. 10.1177/2515245918771304
    https://doi.org/10.1177/2515245918771304 [Google Scholar]
  29. Krings, Hans-Peter
    1986Was in den Köpfen von Übersetzern vorgeht. Tübingen: Narr.
    [Google Scholar]
  30. 2001Repairing Texts: Empirical Investigations of Machine Translation Post-Editing Processes. Ohio: Kent State University Press
    [Google Scholar]
  31. Kruger, Haidee
    2016 “What’s Happening when Nothing’s Happening? Combining eyetracking and keylogging to explore cognitive processing during pauses in translation production.” Across Languages and Cultures17 (1): 25–52. 10.1556/084.2016.17.1.2
    https://doi.org/10.1556/084.2016.17.1.2 [Google Scholar]
  32. Lacruz, Isabel
    2017 “Cognitive Effort in Translation, Editing and Post-Editing.” InHandbook of Translation and Cognition. Edited byJohn Schwieter and Aline Ferreira. 386–401. Malden, MA: John Wiley & Sons. 10.1002/9781119241485.ch21
    https://doi.org/10.1002/9781119241485.ch21 [Google Scholar]
  33. Lacruz, Isabel, Gregory Shreve and Erik Angelone
    2012 “Average Pause Ratio as an Indicator of Cognitive Effort in Post-Editing: A case study.” Proceedings of the AMTA 2012 Workshop on Post-editing Technology and Practice. Association for Machine Translation in the Americas, 29–38.
    [Google Scholar]
  34. Lacruz, Isabel, Michael Denkowski and Alon Lavie
    2014 “Cognitive Demand and Cognitive Effort in Post-Editing”. Paper presented at the11th Conference of the Association for Machine Translation in the Americas-Third Workshop on Post-Editing Technology and Practice, 22–26 October, 2014, Vancouver BC, Canada.
  35. Lacruz, Isabel and Gregory Shreve
    2014 “Pauses and Cognitive Effort in Post-editing. InPost-Editing of Machine Translation: Processes and Applications. Edited bySharon O’Brien, Laura Winther Balling, Michael Carl, Michel Simard, Lucia Specia. 246–274. Cambridge: Cambridge Scholars Publishing.
    [Google Scholar]
  36. Langacker, Ronald W.
    1987Foundations of Cognitive Linguistics, vol. I, Theorical Prerequisites. Stanford: Stanford University Press.
    [Google Scholar]
  37. 1991Foundations of Cognitive Linguistics, vol. II, Descriptive Application. Stanford: Stanford University Press.
    [Google Scholar]
  38. Leijten, Mariëlle & Luuk Van Waes
    2013 “Keystroke Logging in Writing Research: Using Inputlog to analyze writing processes”. Written Communication30 (3): 358–392. 10.1177/0741088313491692
    https://doi.org/10.1177/0741088313491692 [Google Scholar]
  39. Massey, Gary and Maureen Ehrensberger-Dow
    2013 “Evaluating Tanslation Processes: Opportunities and challenges”. InNew Prospects and Perspectives for Educating Language Mediators. Edited byDon Kiraly, Silvia Hansen-Schirra and Karin Maksymski. 157–180. Tübingen: Gunter Narr.
    [Google Scholar]
  40. Mellinger, Christopher
    2014 Computer-assisted Translation: An Empirical investigation of cognitive effort. Ph.D. dissertation, Kent State University, Kent, OH.
    [Google Scholar]
  41. Moorkens, Joss and Andy Way
    2016 “Comparing Translator Acceptability of TM and SMT Outputs.” The Baltic Journal of Modern Computing4: 141–151.
    [Google Scholar]
  42. Muñoz Martín, Ricardo
    2014 “A Blurred Snapshot of Advances in Translation Process Research.” MonTI Special Issue-Minding Translation: 49–84.
    [Google Scholar]
  43. Muñoz Martín, Ricardo and Jose M. Cardona Guerra
    2018 “Translating in Fits and Starts: Pause thresholds and roles in the research of translation processes.” Perspectives: Studies in Translatology. doi:  10.1080/0907676X.2018.1531897
    https://doi.org/10.1080/0907676X.2018.1531897 [Google Scholar]
  44. Muñoz Martín, Ricardo and Kairong Xiao
    (Eds.) 2020 “Cognitive Translation Studies: Theoretical models and methodological criticism.” Linguistica Antverpiensia, New Series-Themes in Translation Studies, 19.
    [Google Scholar]
  45. O’Brien, Sharon
    2006 “Pauses as Indicators of Cognitive Effort in Post-Editing Machine Translation Output.” Across Languages and Cultures7 (1): 1–21. 10.1556/Acr.7.2006.1.1
    https://doi.org/10.1556/Acr.7.2006.1.1 [Google Scholar]
  46. 2007 “An Empirical Investigation of Temporal and Technical Post-Editing Effort.” Translation and Interpreting Studies: 83–136. 10.1075/tis.2.1.03ob
    https://doi.org/10.1075/tis.2.1.03ob [Google Scholar]
  47. 2008 “Processing Fuzzy Matches in Translation Memory Tools: An eye tracking analysis.” InLooking at Eyes: Eye-Tracking Studies of Reading and Translation Processing. Edited bySusanne Göpferich, Arnt Lykke Jakobsen, and Inger M. Mees. 79–102. Copenhagen: Samfundslitteratur 2008.
    [Google Scholar]
  48. R Core Team
    R Core Team 2018R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved fromhttps://www.R-project.org/
    [Google Scholar]
  49. Schaeffer, Moritz and Michael Carl
    2013 “Shared Representations of the Translation Process: A recursive model.” Translation and Interpreting Studies8 (2): 169–190. 10.1075/tis.8.2.03sch
    https://doi.org/10.1075/tis.8.2.03sch [Google Scholar]
  50. 2014 “Measuring the Cognitive Effort of Literal Translation Processes.” Workshop on Human and Computer-assisted Translation, 29–37. Gothenburg, Sweden: Association for Computational Linguistics. 10.3115/v1/W14‑0306
    https://doi.org/10.3115/v1/W14-0306 [Google Scholar]
  51. Screen, Benjamin
    2018 “What Effect Does Post-Editing Have on the Translation Product from an End-User’s Perspective?” Jostrans31: 133–157.
    [Google Scholar]
  52. Stan Development Team
    Stan Development Team 2018Stan Modeling Language Users Guide and Reference Manual (Version 2.18.0). Stan Development Team. Retrieved frommc-stan.org
    [Google Scholar]
  53. Tirkkonen-Condit, Sonja
    2005 “The Monitor Model Revisited: Evidence from process research.” META50 (2): 405–414. 10.7202/010990ar
    https://doi.org/10.7202/010990ar [Google Scholar]
  54. Tirkkonen-Condit, Sonja, Jukka Mäkisalo and Sini Immonen
    2008 “The Translation Process-Interplay between literal rendering and a search for sense.” Across Languages and Cultures9 (1): 1–17. 10.1556/Acr.9.2008.1.1
    https://doi.org/10.1556/Acr.9.2008.1.1 [Google Scholar]
  55. Vandepitte, Sonia, Hartsuiker, Robert J. and Eva Van Assche
    2015 “Process and Text Studies of a Translation Problem”. InPsycholinguistic and Cognitive Inquiries into Translation and Interpreting. Edited byAline Ferreira, and John W. Schwieter. 127–143. Philadelphia: John Benjamins. 10.1075/btl.115.06van
    https://doi.org/10.1075/btl.115.06van [Google Scholar]
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