Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
newsela.com
- 2.
The authors would like to thank MetaMetrics® for their permission to publish Lexile scores in the present chapter. https://www.metametricsinc.com/lexile-framework-reading
References
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.
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.
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.
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7. http://CRAN.R-project.org/package=lme4
Burnham, K., & Anderson, D. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261–304.
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).
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.
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).
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).
Ehrensberger-Dow, M., & Perrin, D. (2009). Capturing translation processes to access metalinguistic awareness. Across Languages and Cultures, 20(2), 275–288.
Fox, J. (2003). Effect displays in R for generalised linear models. Journal of Statistical Software, 8(15), 1–27. http://www.jstatsoft.org/v08/i15/
Garcia, I. (2011). Translating by post-editing: Is it the way forward? Machine Translation, 25, 217–237.
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.
Goldstein, H., & Healey, M. (1995). The graphical presentation of a collection of means. Journal of the Royal Statistical Society, 158, 175–177.
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.
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.
Jakobsen, A., & Schou, L. (1999). Translog documentation. In G. Hansen (Ed.), Probing the process in translation: Methods and results (pp. 1–36). Frederiksberg: Samfundslitteratur.
Krings, H. (2001). Repairing texts. Empirical investigations of machine translation post-editing processes. Kent, OH: Kent State University Press.
Kuznetsova, A., Brockhoff, P., & Christensen, R. (2014). lmerTest: Tests in linear mixed effects models. R package version 2.0-20. http://CRAN.R-project.org/package=lmerTest
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.
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.
Lemhöfer, K., & Broersma, M. (2012). Introducing LexTALE: A quick and valid lexical test for advanced learners of English. Behavior Research Methods, 44, 325–343.
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.
Och, F., & Ney, H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19–51.
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.
R Core Team. (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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. https://doi.org/10.1007/978-3-319-20358-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-20358-4_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20357-7
Online ISBN: 978-3-319-20358-4
eBook Packages: Computer ScienceComputer Science (R0)