Abstract
This chapter reports the results of a longitudinal study (LS14) in which the CASMACAT post-editing workbench was tested with interactive translation prediction (ITP). Whereas previous studies with the CASMACAT workbench (Sanchis-Trilles et al., Machine Translation, 2014) or similar systems (Langlais et al., Machine Translation, 15, 77–98, 2004) tested user interaction only for a few days, the aim of this study was primarily to find out whether and how the performance of professional post-editors improved over time when working with the CASMACAT ITP feature. We were also interested in uncovering any specific profiles of translators depending on personal factors such as previous experience in post-editing and typing skills. Finally, the aim was also to collect feedback from the post-editors in order to know more about their views regarding this type of technology.
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Notes
- 1.
Field trials of the CASMACAT workbench were carried out at Celer Soluciones SL, Madrid, who were partner in the CASMACAT consortium
- 2.
See also Chap. 3 for a comparison of online learning and active learning in the CASMACAT tool.
- 3.
- 4.
The TPR-DB is available online free of charge from: http://sourceforge.net/projects/tprdb/. The TPR-DB website is at: https://sites.google.com/site/centretranslationinnovation/tpr-db.
- 5.
More specific data on the participants’ age, level of experience, professional education, etc., is available in the CRITT TPR Database (metadata folder).
- 6.
Further insights on this metrics are reported in Chap. 2
- 7.
- 8.
For more detailed information on the CFT14 data see Chap. 7.
- 9.
A comparison of the PIO mode and active learning is discussed in Chap. 3, this volume.
- 10.
See Chap. 2 in this volume for more details on these metrics.
- 11.
See also Chaps. 9 and 13 in this volume for a discussion on word translation literality, and how the Cross and the HTra features are indicators for this end.
- 12.
The questionnaire used to collect the user feedback presented in this section is available at this introductory questionnaire .
References
Carl, M., Martinez, M.G., Mesa-Lao, B., Underwood, N., Keller, F., & Hill, R. (2013). CASMACAT project tech report: D1.2: Progress report on user interface studies, cognitive and user modeling. European Commission.
Langlais, P., Lapalme, G., & Loranger, M. (2004). Transtype: Development evaluation cycles to boost translators productivity. Machine Translation, 15, 77–98.
Sanchis-Trilles, G., Alabau, V., Buck, C., Carl, M., Casacuberta, F., García-Martínez, M., et al. (2014). Interactive translation prediction versus conventional post-editing in practice: A study with the CasMaCat workbench. Machine Translation, 28(3–4), 217–235.
Singla, K., Carmona, David O., Gonzales, A., Carl, M., & Bangalore, S. (2013). Predicting post-editor profiles from the translation process. In Proceedings of the Workshop on Interactive and Adaptive Machine Translation, AMTA Workshop, Vancouver, Canada (pp. 51–60).
Acknowledgements
The work described in this chapter was carried out under the auspices of the EU project CASMACAT: Cognitive Analysis and Statistical Methods for Advanced Computer Aided Translation, supported by the European Union 7th Framework Programme Project 287576 (ICT-2011.4.2). Website: http://www.casmacat.eu.
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Alabau, V. et al. (2016). Learning Advanced Post-editing. 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_5
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