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Part of
Eye Tracking and Multidisciplinary Studies on Translation
Edited by Callum Walker and Federico M. Federici
[Benjamins Translation Library 143] 2018
► pp. 97–120

Chapter 6
Recognition and characterization of translator attributes using sequences of fixations and keystrokes

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As the demand for high quality translation continues to increase, there is a growing interest in developing effective computer-assisted tools that support translators in their routines. However, the development of such tools requires a deeper understanding of the translation process that allows us to identify the motoric, perceptual and cognitive bottlenecks of the task. With the ubiquitous presence of computers and logging software, researchers could record the keystrokes of translators, which allowed us to analyze the motor activities of text production. However, the translation process is much more complex and keystroke logging can only capture a marginal fraction of the perceptual and cognitive activities of translators, which are often the source of most translation effort. In recent years, eye-tracking devices became more affordable and they were integrated in our logging interfaces, thus giving us access to both eye-movement and keystroke events during a translation session. Here we present a framework where we use keystrokes and eye movements as low-level measurements of translation behavior. These measurements are then interpreted as sequences of higher-level activities that we use to create interpretable models of translation. The parameters of these models are estimated with the objective to maximize the recognition accuracy of several translator personal attributes given measurements of their translation behavior. These classifier models can then be queried with the purpose to retrieve the most characterizing features of translators that distinguish a certain target personal attribute. In this chapter we formalize this framework, show its effectiveness in a real task and point the reader towards possible extensions of this work.
Article outline
  • 1.Introduction
  • 2.Related work
  • 3.Methodology
    • 3.1Hypothesis statement
    • 3.2Building a predictor
      • 3.2.1Group sequences of events into translation activities
        • Predefined activities
        • Latent activities
      • 3.2.2Recognition of translator expertise
  • 4.Experiments
    • 4.1Experimental conditions
    • 4.2Results
    • 4.3Characterization of translator expertise
    • 4.4Ablating gaze information
    • 4.5Fine-grained characterisation of translators
  • 5.Discussion and future work
  • 6.Conclusions
  • Notes
  • References
Published online: 16 October 2018
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https://doi.org/10.1075/btl.143.06mar
References (31)
References
Armentano, Marcelo G., and Analia A. Amandi. 2012. “Modeling Sequences of User Actions for Statistical Goal Recognition.” User Modeling and User-Adapted Interaction, 22 (3): 281–311.DOI logoGoogle Scholar
Barrett, Maria, and Anders Søgaard. 2015. “Using Reading Behavior to Predict Grammatical Functions.” In Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning, 1–5. Lisbon, Portugal, September. Association for Computational Linguistics.DOI logoGoogle Scholar
Barrett, Maria, Joachim Bingel, Frank Keller, and Anders Søgaard. 2016. “Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data.” In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 579–584. Berlin, Germany, August. Association for Computational Linguistics.DOI logoGoogle Scholar
Bauer, Mathias. 1999. “From Interaction Data to Plan Libraries: A Clustering Approach.” In IJCAI, volume 99, 962–967.Google Scholar
Blaylock, Nate, and James Allen. 2005. “Recognizing Instantiated Goals Using Statistical Methods.” In IJCAI Workshop on Modeling Others from Observations (MOO-2005), 79–86.Google Scholar
Breiman, Leo. 2001. “Random Forests.” Machine learning, 45 (1): 5–32.DOI logoGoogle Scholar
Carl, Michael. 2009. “Triangulating Product and Process Data: Quantifying Alignment Units With Keystroke Data.” In Methodology, Technology and Innovation in Translation Process Research, ed. by Inger M. Mees, Fabio Alves, and Susanne Göpferich, 225–247. Frederiksberg: Samfundslitteratur.Google Scholar
. 2012a. “The CRITT TPR-DB 1.0.” Association for Machine Translation in the Americas (AMTA), 9–18.Google Scholar
. 2012b. “Translog-II: A Program for Recording User Activity Data for Empirical Reading and Writing Research.” In LREC, 4108–4112.Google Scholar
Carl, Michael, Arnt Lykke Jakobsen, and Kristian T. H. Jensen. 2008. “Studying Human Translation Behavior with User-activity Data.” In Proceedings of the 5th International Workshop on Natural Language Processing and Cognitive Science, 114–123.Google Scholar
Carl, Michael, and Martin Kay. 2011. “Gazing and Typing Activities During Translation: A Comparative Study of Translation Units of Professional and Student Translators.” Meta: Translators’ Journal 56 (4): 952–975.DOI logoGoogle Scholar
Carl, Michael, and Matthias Buch-Kromann. 2010. “Correlating Translation Product and Translation Process Data of Professional and Student Translators.” In Proceedings of EAMT, Saint-Raphael, France.Google Scholar
Dechert, Hans W., and Ursula Sandrock. 1986. “Thinking-aloud Protocols: The Decomposition of Language Processing.” In Experimental Approaches to Second Language Learning, ed. by Vivian James Cook, 111–126. Oxford: Pergamon Institute of English.Google Scholar
Doherty, Stephen, Sharon OBrien, and Michael Carl. 2010. “Eye Tracking As an MT Evaluation Technique.” Machine translation 24 (1): 1–13.DOI logoGoogle Scholar
Dragsted, Barbara, and Michael Carl. 2013. “Towards a Classification of Translation Styles Based on Eye-tracking and Keylogging Data.” Journal of Writing Research 5 (1): 133–158.DOI logoGoogle Scholar
Ericsson, K. Anders, and Herbert A. Simon. 1980. “Verbal Reports as Data.” Psychological review 87 (3): 215.DOI logoGoogle Scholar
Gerloff, Pamela. 1986. “Second Language Learners’ Reports on the Interpretive Process: Talk-aloud Protocols of Translation.” In Interlingual and Intercultural Communication, ed. by Juliane House, 243–262. Tubingen: Gunter Narr.Google Scholar
Isaacowitz, Derek M., Heather A. Wadlinger, Deborah Goren, and Hugh R. Wilson. 2006. “Selective Preference in Visual Fixation Away From Negative Images in Old Age? An Eye-tracking Study.” Psychology and Aging 21 (1): 40.DOI logoGoogle Scholar
Jakobsen, Arnt Lykke. 2011. “Tracking Translators’ Keystrokes and Eye Movements with Translog.” In Methods and Strategies of Process Research: Integrative Approaches in Translation Studies, ed. by Cecilia Alvstad, Adelina Hild, and Elisabet Tiselius, 37–55. Amsterdam: John Benjamins.DOI logoGoogle Scholar
. 2003. “Effects of Think Aloud on Translation Speed, Revision, and Segmentation.” In Triangulating Translation: Perspectives in Process Oriented Research, ed. by Fabio Alves, 69–96. Amsterdam: John Benjamins.DOI logoGoogle Scholar
Jakobsen, Arnt Lykke, and Lasse Schou. 1999. “Translog Documentation.” In Probing the Process in Translation: Methods and Results, ed. by Gyde Hansen, 1–36. Frederiksberg: Samfundslitteratur.Google Scholar
Just, Marcel Adam, and Patricia A. Carpenter. 1980. “A Theory of Reading: From Eye Fixations to Comprehension.” Psychological Review 87: 329–354.DOI logoGoogle Scholar
Klerke, Sigrid, Yoav Goldberg, and Anders Søgaard. 2016. “Improving Sentence Compression by Learning to Predict Gaze.” In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1528–1533. San Diego, California, June. Association for Computational Linguistics.Google Scholar
Kock, Mirjam, and Alexandros Paramythis. 2011. “Activity Sequence Modelling and Dynamic Clustering for Personalized E-learning.” User Modeling and User-Adapted Interaction 21 (1–2): 51–97.DOI logoGoogle Scholar
Krings, Hans Peter. 1986. Was in den Kopfen von Ubersetzern vorgeht: Eine empirische Untersuchung zur Struktur des Ubersetzungsprozesses an fortgeschrittenen Franzosischlernern. Tübingen: Narr.Google Scholar
. 2001. Repairing Texts: Empirical Investigations of Machine Translation Post-editing Processes. Kent, Ohio: Kent State University Press.Google Scholar
Liaw, Andy, and Matthew Wiener. 2002. “Classification and Regression by randomForest.” R News 2 (3): 18–22.Google Scholar
Maechler, Martin, Peter Rousseeuw, Anja Struyf, Mia Hubert, and Kurt Hornik, 2013. Cluster: Cluster Analysis Basics and Extensions. R Package Version 1.14.4.Google Scholar
Martínez-Gómez, Pascual, Tadayoshi Hara, and Akiko Aizawa. 2012. “Recognizing Personal Characteristics of Readers Using Eye-movements and Text Features.” In Proceedings of COLING 2012, 1747–1762. Mumbai, India, December.Google Scholar
Martínez-Gómez, Pascual, and Akiko Aizawa. 2014. “Recognition of Understanding Level and Language Skill Using Measurements of Reading Behavior.” In Proceedings of the 19th International Conference on Intelligent User Interfaces (IUI), 95–104. Haifa, Israel, February. Association for Computing Machinery.DOI logoGoogle Scholar
Schubert, Klaus. 2007. Wissen, Sprache, Medium, Arbeit. Ein integratives Modell der ein- und mehrsprachigen Fachkommunikation. Tübingen: Narr.Google Scholar
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