Recognition of translator expertise using sequences of fixations and keystrokes

Abstract

Professional human translation is necessary to meet high quality standards in industry and governmental agencies. Translators engage in multiple activities during their task, and there is a need to model their behavior, with the objective to understand and optimize the translation process. In recent years, user interfaces enabled us to record user events such as eye-movements or keystrokes. Although there have been insightful descriptive analysis of the translation process, there are multiple advantages in enabling quantitative inference. We present methods to classify sequences of fixations and keystrokes into activities and model translation sessions with the objective to recognize translator expertise. We show significant error reductions in the task of recognizing certified translators and their years of experience, and analyze the characterizing patterns.