Predicting translation behaviorsby using Hidden Markov Model

Lu Sheng | Renmin University of China

Michael Carl | Kent State University

Yao Xinyue | Renmin University of China

Su Wenchao | 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

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.

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.

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.

Carl, Michael and Arnt Lykke Jakobsen

2009 “Towards Statistical Modelling of Translators’ Activity Data.” International Journal of Speech Technology 12: 125–138.

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.

2018 “The Development of the TPR-DB as Grounded Theory Method.” Translation, Cognition & Behavior 1 (1): 168–193.

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.

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.

[ p. 98 ]

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.

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.

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).

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.

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.

[ p. 99 ]

Cited by

Cited by other publications

Su, Wenchao

2020. In Eye-Tracking Processes and Styles in Sight Translation [New Frontiers in Translation Studies, ], ► pp. 9 ff.

This list is based on CrossRef data as of 05 october 2020. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.