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The gaze behaviour of a reader is helpful in solving several NLP tasks suchas automatic essay grading. However, collecting gaze behaviour from readers iscostly in terms of time and money. In this paper, we propose a way to improveautomatic essay grading using gaze behaviour, which is learnt at run time usinga multi-task learning framework. To demonstrate the efficacy of this multi-tasklearning based approach to automatic essay grading, we collect gaze behaviourfor 48 essays across 4 essay sets, and learn gaze behaviour for the rest of theessays, numbering over 7000 essays. Using the learnt gaze behaviour, we canachieve a statistically significant improvement in performance over thestate-of-the-art system for the essay sets where we have gaze data. We alsoachieve a statistically significant improvement for 4 other essay sets,numbering about 6000 essays, where we have no gaze behaviour data available.Our approach establishes that learning gaze behaviour improves automatic essaygrading.

Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour