Article published In: Translation, Cognition & Behavior
Vol. 3:1 (2020) ► pp.76–99
Predicting translation behaviors by using Hidden Markov Model
Published online: 13 May 2020
https://doi.org/10.1075/tcb.00035.lu
https://doi.org/10.1075/tcb.00035.lu
Abstract
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.
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
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Carl, Michael, Yuxiang Wei, Sheng Lu, Longhui Zou, Takanori Mizowaki & Masaru Yamada
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