Article published in:
Translation, Cognition & Behavior
Vol. 3:1 (2020) ► pp. 76–99

Predicting translation behaviors by using Hidden Markov Model

| Renmin University of China
| Kent State University
| Renmin University of China
| Guangdong University of Foreign Studies
The translation process can be studied as sequences of activity units. The application of machine learning technology offers researchers new possibilities in the study of the translation process. This research project developed a program, activity unit predictor, using the Hidden Markov Model. The program takes in duration, translation phase, target language and fixation as the input and produces an activity unit type as the output. The highest prediction accuracy reached is 61%. As one of the first endeavors, the program demonstrates strong potential of applying machine learning in translation process research.
Keywords: translation process research, translation behavior, activity unit, Hidden Markov Model
Article outline
  • 1.Introduction
  • 2.Translation process modeling
  • 3.Activity unit and activity unit predictor
  • 4.The present study
    • 4.1Data analysis
    • 4.2Modeling
      • 4.2.1Model configuration
      • 4.2.2Decoding
      • 4.2.3Generalization
    • 4.3Experiment
  • 5.Results
  • 6.Discussion and conclusion
  • Note
  • References
Published online: 13 May 2020
https://doi.org/10.1075/tcb.00035.lu
References
Aziz, Wilker, Maarit Koponen, and Lucia Specia
2014 “Sub-sentence Level Analysis of Machine Translation Post-editing Effort.” In Post-editing of Machine Translation: Processes and applications. Edited by S. O’Brien, L. W. Balling, M. Carl, M. Simard and L. Specia, 170–199. Newcastle: Cambridge Scholars.Google Scholar
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio
2015 “Neural Machine Translation by Jointly Learning to Align and Translate.” Paper presented at International Conference on Learning Representations (San Diego, USA, 7–9 May 2015).
Bangalore, Srinivas, Bergljot Behrens, Michael Carl, Maheshwar Ghankot, Arndt Heilmann, Jean Nitzke, Moritz J. Schaeffer, and Annegret Sturm
2016 “Syntactic Variance and Priming Effects in Translation.” In New Directions in Empirical Translation Process Research. Edited by M. Carl, S. Bangalore and M. Schaeffer, 211–238. Cham: Springer International. CrossrefGoogle Scholar
Campbell, Stuart
2000 “Choice Network Analysis in Translation Research.” In Intercultural faultlines: Research models in translations studies. Edited by M. Olohan, 29–42. Manchester: St. Jerome.Google Scholar
Carl, Michael and Arnt Lykke Jakobsen
2009 “Towards Statistical Modelling of Translators’ Activity Data.” International Journal of Speech Technology 121: 125–138. CrossrefGoogle Scholar
Carl, Michael and Moritz J. Schaeffer
2017 “Sketch of a Noisy Channel Model for the Translation Process.” In Empirical modelling of translation and interpreting. Edited by S. Hansen-Schirra, O. Czulo, and S. Hofmann, 71–116. Berlin: Language Science Press.Google Scholar
2018 “The Development of the TPR-DB as Grounded Theory Method.” Translation, Cognition & Behavior 1 (1): 168–193. CrossrefGoogle Scholar
Carl, Michael, Srinivas Bangalore and Moritz J. Schaeffer
2016a “Introduction and Overview.” In New Directions in Empirical Translation Process Research. Edited by M. Carl, S. Bangalore and M. Schaeffer, 3–12. Cham: Springer International. CrossrefGoogle Scholar
2016b “The CRITT Translation Process Research Database.” In New Directions in Empirical Translation Process Research. Edited by M. Carl, S. Bangalore and M. Schaeffer, 13–54. Cham: Springer International. CrossrefGoogle Scholar
Heilmann, Arndt and Stella Neumanm
2016 “Dynamic Pause Assessment of Keystroke Logged Data for the Detection of Complexity in Translation and Monolingual Text Production.” Paper presented at Workshop on Computational Linguistics for Linguistic Complexity (Osaka, Japan, 11–17 December 2016).
Lacruz, Isabel and Gregory Shreve
2014 “Pauses and Cognitive Effort in Post-Editing.” In Post-editing of Machine Translation: Processes and applications. Edited by S. O’Brien et al., 246–273. Newcastle: Cambridge Scholars.Google Scholar
Läubli, Samuel and Ulrich Germann
2016 “Statistical Modelling and Automatic Tagging of Human Translation Processes.” In New Directions in Empirical Translation Process Research. Edited by M. Carl, S. Bangalore and M. Schaeffer, 155–181. Cham: Springer International. CrossrefGoogle Scholar
Martínez Gómez, Pascual Akshay Minocha, Jin Huang, Michael Carl, Srinivas Bangalore, and Akiko Aizawa
2014 “Recognition of Translator Expertise using Sequences of Fixations and Keystrokes.” Paper presented at Eye Tracking Research and Applications Symposium (Safety Habor, USA, 26–28 March 2014). Crossref
Minh, Volodymyr et al.
2014 “Recurrent Models of Visual Attention.” Paper presented at Neural Information Processing Systems (Montreal, 8–13 December 2014).
Pomegranate: Probabilistic Modeling in Python
https://​homes​.cs​.washington​.edu​/~jmschr​/lectures​/pomegranate​.html Accessed 20 August 2018.
Schaeffer, Moritz J., Michael Carl, Isabel Lacruz, and Akiko Aizawa
2016 “Measuring Cognitive Translation Effort with Activity Units.” Baltic Journal of Modern Computing 4 (2): 331–345.Google Scholar
Toury, Gideon
2004 “Probabilistic Explanations in Translation Studies: Welcome as they are, would they qualify as universals?” In Translation Universals: Do they exist. Edited by A. Mauranen and P. Kujamäki, 15–32. Amsterdam: John Benjamins. CrossrefGoogle Scholar
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