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A Computational Cognitive Model of Human Translation Processes
Abstract
Human translation process research analyzes the translation behavior of translators, such as memory and search strategies to solve translation problems, types of units that translators focus on, etc., identifies the temporal (and/or contextual) structure of those activities, and describes inter- and intra-personal variation. Various models have been developed that explain translators’ behavior in terms of controlled and uncontrolled workspaces and with micro- and macro-translation strategies. However, only a few attempts have been made to ground and quantify translation process models in empirical user activity data. In order to close this gap, this chapter outlines a computational framework for a cognitive model of human translation. The authors investigate the structure of the translators’ keystrokes and gaze data, discuss possibilities for their classification and visualization, and explain how a translation model can be grounded and trained on the empirical data. The insight gained from such a computational translation model not only enlarges our knowledge about human translation processes, but also has the potential to enhance the design of interactive MT systems and help interpret user activity data in human-MT system interaction.
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