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Post-editing of machine translation is gaining popularity as a solution to the ever-increasing demands placed on human translators. There has been a great deal of research in this area aimed at determining the feasibility of post-editing and at predicting post-editing effort based on source-text features and machine translation errors. However, considerably less is known about the mental workings of post-editing and post-editors’ decision-making or, in particular, the relationship between post-editing effort and different mental processes. This paper investigates these issues by analysing data from a think-aloud study through the lens of eye movements and subjective ratings obtained in a separate task. The results show that mental processes associated with grammar and lexis are significantly associated with cognitive effort in post-editing. This association was not observed for other aspects of the task concerning, for example, discourse or the real-world use of the text. In addition, it was noted that lexical issues are linked to long sequences of thought processes. The paper shows that lexis plays a central role in post-editing, and argues that more emphasis should be placed on this issue in future research and in post-editor training.

  • Alves, F., Koglin, A., Mesa-Lao, B., Martínez, M. G., Fonseca, N. B. L., , A. M., Gonçalves, J. L., Szpak, K. S., Sekino, K., & Aquino, M. 2016. Analysing the Impact of Interactive Machine Translation on Post-editing Effort. In: Carl, M., Bangalore, S. & Schaeffer, M. (eds) New Directions in Empirical Translation Process Research: Exploring the CRITT TPR-DB. Heidelberg: Springer. 77-94.

    • Search Google Scholar
    • Export Citation
  • Aziz, W., Castilho, S. & Specia, L. 2012. PET: A tool for post-editing and assessing machine translation. Paper presented at the 8th International Conference on Language Resources and Evaluation, 21-27 May, 2012, Istanbul, Turkey.

    • Search Google Scholar
    • Export Citation
  • Baayen, R. H. 2008. Analysing Linguistic Data: A Practical Introduction to Statistics. Cambridge: Cambridge University Press.

  • Baayen, R. H., Davidson, D. J. & Douglas Bates, M. 2008. Mixed-effects Modeling With Crossed Random Effects for Subjects and Items. Journal of Memory and Language Vol. 59. No. 4. 390-412.

    • Search Google Scholar
    • Export Citation
  • Balling, L. W. & Baayen, R. H. 2008. Morphological Effects in Auditory Word Recognition: Evidence from Danish. Language and Cognitive Processes Vol. 23. No. 7-8. 1159-1190.

    • Search Google Scholar
    • Export Citation
  • Bates, D., Maechler, M. & Bolker, B. 2012. lme4: Linear Mixed-effects Models Using S4 Classes. R Package Version 0.999999-0 [computer software]. Retrieved from http://CRAN.Rproject.org/package0lme4.

    • Search Google Scholar
    • Export Citation
  • Carl, M. 2012. Translog-II: a program for recording user activity data for empirical reading and writing research. Paper presented at the 8th International Conference on Language Resources and Evaluation, 21-27 May, 2012, Istanbul, Turkey.

    • Search Google Scholar
    • Export Citation
  • Carl, M., Kay, M. & Jensen, K. T. H. 2010. Long distance revisions in drafting and post-editing. Paper presented at the 11th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing-2010, Iasi, Romania, March 21–27, 2010.

    • Search Google Scholar
    • Export Citation
  • Creswell, J. W. 2009. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Los Angeles: Sage.

  • de Almeida, G. 2013. Translating the post-editor: an investigation on post-editing changes and correlations with professional experience across two Romance languages. PhD Thesis. Dublin: Dublin City University.

    • Search Google Scholar
    • Export Citation
  • Denkowski, M. & Lavie, A. 2011. Meteor 1.3: automatic metric for reliable optimization and evaluation of machine translation systems. Paper presented at the 6th Workshop on Statistical Machine Translation (EMNLP 2011), July 30–31, 2011, Edinburgh, UK.

    • Search Google Scholar
    • Export Citation
  • Ericsson, K. A. & Simon, H. A. 1980. Verbal Reports as Data. Psychological Review Vol. 87. No. 3. 215-251.

  • Green, S., Heer, J. & Manning, C. D. 2013. The efficacy of human post-editing for language translation. Paper presented at the SIGCHI Conference on Human Factors in Computing Systems, 27 April - 2 May, 2013, Paris, France.

    • Search Google Scholar
    • Export Citation
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. & Witten, I. H. 2009. The WEKA Data Mining Software: an Update. SIGKDD Explorations Vol. 11. No. 1. 10-18.

    • Search Google Scholar
    • Export Citation
  • Hvelplund, K. T. 2011. Allocation of cognitive resources in translation: an eye-tracking and keylogging study. PhD Thesis. Copenhagen: Copenhagen Business School.

    • Search Google Scholar
    • Export Citation
  • Jakobsen, A. L. 2003. Effects of Think Aloud on Translation Speed, Revision and Segmentation. In: Alves, F. (ed.) Triangulating Translation. Perspectives in Process Oriented Research. Amsterdam: Benjamins. 69-95.

    • Search Google Scholar
    • Export Citation
  • Jones, F. R. 2011. Poetry Translating as Expert Action: Processes, Priorities and Networks. Amsterdam: John Benjamins.

  • Just, M. & Carpenter, P. 1980. A Theory of Reading: from Eye Fixation to Comprehension. Psychological Review Vol. 87. No. 4. 329-354.

  • Koponen, M. 2012. Comparing human perceptions of post-editing effort with post-editing operations. Paper presented at the Seventh Workshop on Statistical Machine Translation, June 7-8, Montréal, Canada.

    • Search Google Scholar
    • Export Citation
  • Koponen, M., Aziz, W., Ramos, L. & Specia, L. 2012. Post-editing time as a measure of cognitive effort. Paper presented at the AMTA 2012 Workshop on Post-Editing Technology and Practice (WPTP 2012), October 28, 2012, San Diego, USA.

    • Search Google Scholar
    • Export Citation
  • Krings, H. P. 2001. Repairing Texts: Empirical Investigations of Machine Translation Post-Editing Processes. Kent, Ohio: Kent State University Press.

    • Search Google Scholar
    • Export Citation
  • Lacruz, I., Denkowski, M. & Lavie, A. 2014. Cognitive demand and cognitive effort in post-editing. Paper presented at the 11th Conference of the Association for Machine Translation in the Americas — Third Workshop on Post-EditingTechnology and Practice, 22–26 October, 2014, Vancouver BC,Canada.

    • Search Google Scholar
    • Export Citation
  • Landis, J. R. & Koch, G. G. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics Vol. 33. 159-174.

  • MacQueen, J. 1967. Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, California: University of Calfornia Press. 281-297.

    • Search Google Scholar
    • Export Citation
  • Meara, P. & Buxton, B. 1987. An Alternative to Multiple Choice Vocabulary Tests. Language Testing Vol. 4. No. 2. 142-154.

  • Moray, N. 1967. Where is Capacity Limited? A Survey and a Model. Acta Psychologica Vol. 27. 84-92.

  • Newmark, P. 1988. A Textbook of Translation. Hertfordshire: Prentice Hall International.

  • O’Brien, S. 2011. Towards Predicting Post-editing Productivity. Machine Translation Vol. 25. No. 3. 197-215.

  • O’Donnell, R. D. & Eggemeier, F. T. 1986. Workload Assessment Methodology. In: Boff, K. R., Kaufman, L. & Thomas, J. P. (eds) Handbook of Perception and Human Performance. New York: John Wiley and Sons. 42-49.

    • Search Google Scholar
    • Export Citation
  • Paas, F. G. 1992. Training Strategies for Attaining Transfer of Problem-solving Skill in Statistics: A Cognitive-load Approach. Journal of Educational Psychology Vol. 84. No. 4. 429-434.

    • Search Google Scholar
    • Export Citation
  • Paas, F., Tuovinen, J. E., Tabbers, H. & van Gerven, P. W. M. 2003. Cognitive Load Measurement as a Means to Andvance Cognitive Load Theory. Educational Psyhologist Vol. 38. No. 1. 63-71.

    • Search Google Scholar
    • Export Citation
  • Read, J. 2007. Second Language Vocabulary Assessment: Current Practices and New Directions. International Journal of English Studies Vol. 7 No. 2. 105-126.

    • Search Google Scholar
    • Export Citation
  • Specia, L. 2011. Exploiting objective annotations for measuring translation post-editing effort. Paper presented at the 15th International Conference of the European Association for Machine Translation, 30-31 May, 2011, Leuven, Belgium.

    • Search Google Scholar
    • Export Citation
  • Sun, S. 2011. Think-Aloud-Based Translation Process Research: Some Methodological Considerations. Meta Vol. 56. No. 4. 928-951.

  • TAUS/CNGL. 2010. Machine Translation Postediting Guidelines. Accessed 7 November 2014. https://evaluation.taus.net/resources/guidelines/post-editing/machine-translation-postediting-guidelines.

  • Temnikova, I. 2010. A cognitive evaluation approach for a controlled language post-editing experiment. Paper presented at the 7th International Conference on Language Resources and Evaluation, 17–23 May, 2010, Valetta, Malta.

    • Search Google Scholar
    • Export Citation
  • Tyler, S. W., Hertel, P. T., McCallum, M. C. & Hellis, H. C. 1979. Cognitive Effort and Memory. Journal of Experimental Psychology: Human Learning and Memory Vol. 5. No. 6. 607-617.

    • Search Google Scholar
    • Export Citation
  • Vieira, L.N. 2014. Indices of Cognitive Effort in Machine Translation Post-editing. Machine Translation. Vol. 28. No. 3-4. 187-216.

  • Vieira, L.N. 2016. Cognitive effort in post-editing of machine translation: evidence from eye movements, subjective ratings, and think-aloud protocols. PhD Thesis. Newcastle upon Tyne: Newcastle University.

    • Search Google Scholar
    • Export Citation

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