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
2012 “Modeling Sequences of User Actions for Statistical Goal Recognition.” User Modeling and User-Adapted Interaction, 22 (3): 281–311.
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.
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.
Bauer, Mathias
1999 “From Interaction Data to Plan Libraries: A Clustering Approach.” In IJCAI, volume 99, 962–967.
Blaylock, Nate, and James Allen
2005 “Recognizing Instantiated Goals Using Statistical Methods.” In IJCAI Workshop on Modeling Others from Observations (MOO-2005), 79–86.
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.
Carl, Michael
2012a “The CRITT TPR-DB 1.0.” Association for Machine Translation in the Americas (AMTA), 9–18.
Carl, Michael
2012b “Translog-II: A Program for Recording User Activity Data for Empirical Reading and Writing Research.” In LREC, 4108–4112.
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.
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.
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.
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.
Doherty, Stephen, Sharon OBrien, and Michael Carl
2010 “Eye Tracking As an MT Evaluation Technique.” Machine translation 24 (1): 1–13.
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.
Ericsson, K. Anders, and Herbert A. Simon
1980 “Verbal Reports as Data.” Psychological review 87 (3): 215.
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.
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.
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.
1999 “Translog Documentation.” In Probing the Process in Translation: Methods and Results, ed. by Gyde Hansen, 1–36. Frederiksberg: Samfundslitteratur.
Just, Marcel Adam, and Patricia A. Carpenter
1980 “A Theory of Reading: From Eye Fixations to Comprehension.” Psychological Review 87: 329–354.
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.
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.
Krings, Hans Peter
1986Was in den Kopfen von Ubersetzern vorgeht: Eine empirische Untersuchung zur Struktur des Ubersetzungsprozesses an fortgeschrittenen Franzosischlernern. Tübingen: Narr.
Krings, Hans Peter
2001Repairing Texts: Empirical Investigations of Machine Translation Post-editing Processes. Kent, Ohio: Kent State University Press.
Liaw, Andy, and Matthew Wiener
2002 “Classification and Regression by randomForest.” R News 2 (3): 18–22.
Maechler, Martin, Peter Rousseeuw, Anja Struyf, Mia Hubert, and Kurt Hornik
2013Cluster: Cluster Analysis Basics and Extensions. R Package Version 1.14.4.
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.
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.
Schubert, Klaus
2007Wissen, Sprache, Medium, Arbeit. Ein integratives Modell der ein- und mehrsprachigen Fachkommunikation. Tübingen: Narr.
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