Abstract
The present contribution examines an interesting but technically quite imperfect sample recording in the CRITT Centre’s TPR-database in an attempt to demonstrate how imperfect gaze data can be meaningfully reconstructed and to illustrate and explore details of translational keystroke and gaze behaviour in a single translator. The data clearly show that translation proceeds segment by segment. A source text (ST) segment is read, comprehended, and translated. As the translation is typed, we see it emerging segment by segment. Perfectly smooth production of target text across extended stretches of time is not frequently seen, but is often approximated. Highly expert performers are able to bind processing segments together into a flow of continuous production. From their recorded gaze behaviour, we can observe that experts do process text segment by segment, so how is it that they can manage to sometimes maintain fairly continuous production? Evidence of how reading, comprehension, translation, formulation and typing activities are coordinated is found in recorded gaze data, which provide detailed evidence of what ST text unit was being worked on at any given point in time, and evidence provided by keystrokes. These combined sources of evidence can be used to infer both what ST (sub)segment was being processed within what ST context, and in what manner, always with the big unknowns at play of the translator’s knowledge, memory, meaning construction intelligence and expressive power – and the suspicion that the human brain is doing a good deal more than eye movements and keystrokes reveal.
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Notes
- 1.
Gile’s ‘tightrope’ metaphor, which aimed at describing how a simultaneous interpreter operates close to cognitive saturation, having to balance several cognitive efforts at the same time: listening, reformulating, speaking (Gile 1999), could be relevantly considered in relation to expert and optimally fluent translation performance. The data presented below seems to clearly indicate that a translator’s attention may also be split or have to travel extremely fast between reading, reformulating, typing and monitoring to a point where one wonders if such performance is possible with only 7 +/− 2 items of STM capacity available, according to the famous observation by Miller (1956, p. 81).
- 2.
Translog II from 2011 was designed by professor Michael Carl. It was based on the code of the experimental Translog 2006 version designed by the author, programmed by his son Lasse Schou, and used in the EU Eye-to-IT project (2006–2009). Translog II can be freely downloaded at https://sites.google.com/site/centretranslationinnovation/translog-ii
- 3.
- 4.
www.translog.dk. This version is no longer distributed and the site will be closed down in 2018.
- 5.
Keystroke intervals have been calculated from the raw data in the xml file.
- 6.
Segments 16 and 17 have been comprehensively analysed in Jakobsen (2016).
- 7.
Translog II also has a GWM (gaze-to-word mapping) function designed to automatically identify words that are fixated. GWM was not used in the present study.
- 8.
A translation process model could include several modalities, listening, reading and watching at the input end, speaking, writing, signing at the production end.
- 9.
As will be seen in subsequent figures, there is also vertical displacement in the representation of gaze samples and fixations in this recording. Fixations on the headline are well aligned vertically, but the fixations on the second line mostly appear above the line, and the same applies to fixations on lines three and four. Towards the bottom end of the screen we see the reverse phenomenon that the lower on the screen fixations are displayed, the more they tend to be placed lower than their probable target. Inaccurate calibration can also explain why left-eye samples (in red) often appear lower down on the screen than (green) right-eye samples, except in the top and bottom right areas.
- 10.
The interested reader is invited to download a free copy of Translog II and access and replay the P01_T1 file from the SG12 experiment, which is also freely accessible at:https://sites.google.com/site/centretranslationinnovation/
- 11.
A speculation might be that the translator was considering a more technical medical translation of ‘amounts’.
- 12.
Schaeffer (2013) makes an interesting case that literal, word-to-word translation is faster and cognitively easier than translation which involves rearrangement of word order.
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Jakobsen, A.L. (2019). Segmentation in Translation: A Look at Expert Behaviour. In: Li, D., Lei, V., He, Y. (eds) Researching Cognitive Processes of Translation. New Frontiers in Translation Studies. Springer, Singapore. https://doi.org/10.1007/978-981-13-1984-6_4
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