Eye tracking for Machine Translation Quality Evaluation

Abstract

Eye tracking offers unique insights into cognitive processes, making it a promising tool for evaluating machine translation (MT). This study explores the feasibility of using an iPhone 12 camera-based eye tracker with a 14-inch laptop display for conducting translation evaluation in personal workspaces, offering a more accessible and cost-effective alternative to traditional setups. Participants evaluated source sentences, selected translations, and identified problematic words while their gaze metrics were recorded and analyzed. Our findings reveal statistically significant correlations between gaze patterns and preferred translations, as well as increased visual attention to problematic words. These results demonstrate that home-based eye tracking systems are technically sufficient for capturing gaze behavior accurately enough for MT evaluation purposes. A potential practical application is to speed up translation proof-reading using eye tracking technique to automatically mark portions of text that should be attended to and improved based on the gaze pattern during a quick reading.

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