  The gaze behaviour of a reader is helpful in solving several NLP tasks such
as automatic essay grading. However, collecting gaze behaviour from readers is
costly in terms of time and money. In this paper, we propose a way to improve
automatic essay grading using gaze behaviour, which is learnt at run time using
a multi-task learning framework. To demonstrate the efficacy of this multi-task
learning based approach to automatic essay grading, we collect gaze behaviour
for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the
essays, numbering over 7000 essays. Using the learnt gaze behaviour, we can
achieve a statistically significant improvement in performance over the
state-of-the-art system for the essay sets where we have gaze data. We also
achieve 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 essay
grading.
