Skip to main content

The Effectiveness of Consulting External Resources During Translation and Post-editing of General Text Types

  • Chapter

Part of the New Frontiers in Translation Studies book series (NFTS)


Consulting external resources is an important aspect of the translation process. Whereas most previous studies were limited to screen capture software to analyze the usage of external resources, we present a more convenient way to capture this data, by combining the functionalities of CASMACAT with those of Inputlog, two state-of-the-art logging tools. We used this data to compare the types of resources used and the time spent in external resources for 40 from-scratch translation sessions (HT) and 40 post-editing (PE) sessions of 10 master’s students of translation (from English into Dutch). We took a closer look at the effect of the usage of external resources on productivity and quality of the final product. The types of resources consulted were comparable for HT and PE, but more time was spent in external resources when translating. Though search strategies seemed to be more successful when translating than when post-editing, the quality of the final product was comparable, and post-editing was faster than regular translation.


  • Translation
  • Post-editing
  • External resources
  • Translation process
  • Translation quality

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-20358-4_6
  • Chapter length: 23 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-20358-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.00
Price excludes VAT (USA)
Hardcover Book
USD   139.99
Price excludes VAT (USA)
Fig. 6.1
Fig. 6.2
Fig. 6.3
Fig. 6.4
Fig. 6.5
Fig. 6.6
Fig. 6.7
Fig. 6.8


  1. 1.

  2. 2.

    The authors would like to thank MetaMetrics® for their permission to publish Lexile scores in the present chapter.


  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

    MathSciNet  CrossRef  MATH  Google Scholar 

  • Alabau, V., Bonk, R., Buck, C., Carl, M., Casacuberta, F., Martínez, M., et al. (2013). CASMACAT: An open source workbench for advanced computer aided translation. The Prague Bulletin of Mathematical Linguistics, 100, 101–112. doi:10.2478/pralin-2013-0016.

    CrossRef  Google Scholar 

  • Angelone, E. (2010). Uncertainty, uncertainty management and metacognitive problem solving in the translation task. In G. Shreve & E. Angelone (Eds.), Translation and cognition (pp. 17–40). Amsterdam; Philadelphia: Benjamins.

    CrossRef  Google Scholar 

  • Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7.

  • Burnham, K., & Anderson, D. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261–304.

    MathSciNet  CrossRef  Google Scholar 

  • Carl, M. (2012). The CRITT TPR-DB 1.0: A database for empirical human translation process research. In S. O’Brien, M. Simard, & L. Specia (Eds.), Proceedings of the AMTA 2012 workshop on post-editing technology and practice (WPTP 2012) (pp. 9–18). Stroudsburg, PA: Association for Machine Translation in the Americas (AMTA).

    Google Scholar 

  • Carl, M., & Buch-Kromann, M. (2010). Correlating translation product and translation process data of professional and student translators. In Proceedings of EAMT, Saint-Raphaël, France.

    Google Scholar 

  • Daems, J., Macken, L., & Vandepitte, S. (2013). Quality as the sum of its parts: A two-step approach for the identification of translation problems and translation quality assessment for HT and MT+PE. In Proceedings of the MT summit XIV workshop on post-editing technology and practice (pp. 63–71).

    Google Scholar 

  • Daems, J., Macken, L., & Vandepitte, S. (2014). On the origin of errors: A fine-grained analysis of MT and PE errors and their relationship. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the ninth international conference on language resources and evaluation (LREC’14) (pp. 62–66). Reykjavik, Iceland: European Language Resources Association (ELRA).

    Google Scholar 

  • Ehrensberger-Dow, M., & Perrin, D. (2009). Capturing translation processes to access metalinguistic awareness. Across Languages and Cultures, 20(2), 275–288.

    CrossRef  Google Scholar 

  • Fox, J. (2003). Effect displays in R for generalised linear models. Journal of Statistical Software, 8(15), 1–27.

  • Garcia, I. (2011). Translating by post-editing: Is it the way forward? Machine Translation, 25, 217–237.

    CrossRef  Google Scholar 

  • Germann, U. (2008). Yawat: Yet another word alignment tool. In 46th annual meeting of the association for computational linguistics: Human language technologies; demo session, 20–23. Columbus, OH.

    Google Scholar 

  • Goldstein, H., & Healey, M. (1995). The graphical presentation of a collection of means. Journal of the Royal Statistical Society, 158, 175–177.

    CrossRef  Google Scholar 

  • Göpferich, S. (2010). The translation of instructive texts from a cognitive perspective. In F. Alves, S. Göpferich, & I. Mees (Eds.), New approaches in translation process research (pp. 5–65). Frederiksberg: Samfundslitteratur.

    Google Scholar 

  • Jakobsen, A. (2003). Effects of think aloud on translation speed, revision and segmentation. In F. Alves (Ed.), Triangulating translation: Perspectives in process oriented research (pp. 69–95). Amsterdam: Benjamins.

    CrossRef  Google Scholar 

  • Jakobsen, A., & Schou, L. (1999). Translog documentation. In G. Hansen (Ed.), Probing the process in translation: Methods and results (pp. 1–36). Frederiksberg: Samfundslitteratur.

    Google Scholar 

  • Krings, H. (2001). Repairing texts. Empirical investigations of machine translation post-editing processes. Kent, OH: Kent State University Press.

    Google Scholar 

  • Kuznetsova, A., Brockhoff, P., & Christensen, R. (2014). lmerTest: Tests in linear mixed effects models. R package version 2.0-20.

  • Leijten, M., & Van Waes, L. (2013). Keystroke logging in writing research: Using Inputlog to analyze and visualize writing processes. Written Communication, 30(3), 358–392. doi:10.1177/0741088313491692.

    CrossRef  Google Scholar 

  • Leijten, M., Van Waes, L., Schriver, K., & Hayes, J. (2014). Writing in the workplace: Constructing documents using multiple digital sources. Journal of Writing Research, 5(3), 285–337.

    CrossRef  Google Scholar 

  • Lemhöfer, K., & Broersma, M. (2012). Introducing LexTALE: A quick and valid lexical test for advanced learners of English. Behavior Research Methods, 44, 325–343.

    CrossRef  Google Scholar 

  • Macklovitch, E., Lapalme, G., & Gotti, F. (2008). TransSearch: What are translators looking for? In AMTA-2008: MT at work: Proceedings of the eighth conference of the association for machine translation in the Americas (pp. 412–419), Waikiki, Hawai’i, St. Honolulu.

    Google Scholar 

  • Och, F., & Ney, H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19–51.

    CrossRef  MATH  Google Scholar 

  • Plitt, M., & Masselot, F. (2010). A productivity test of statistical machine translation post-editing in a typical localization context. Prague Bulletin of Mathematical Linguistics, 93, 7–16.

    CrossRef  Google Scholar 

  • R Core Team. (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

  • Yamada, M. (2015). Can college students be post-editors? An investigation into employing language learners in machine translation plus post-editing settings. Machine Translation, 29, 49–67.

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Joke Daems .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Daems, J., Carl, M., Vandepitte, S., Hartsuiker, R., Macken, L. (2016). The Effectiveness of Consulting External Resources During Translation and Post-editing of General Text Types. In: Carl, M., Bangalore, S., Schaeffer, M. (eds) New Directions in Empirical Translation Process Research. New Frontiers in Translation Studies. Springer, Cham.

Download citation

  • DOI:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20357-7

  • Online ISBN: 978-3-319-20358-4

  • eBook Packages: Computer ScienceComputer Science (R0)