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.

翻译：读者的凝视行为有助于解决一些NLP任务,如自动作文等级等。 但是,从读者收集凝视行为在时间和金钱上成本高昂。 在本文中,我们提出一种方法,用凝视行为来改进自动作文等级,这是使用多任务学习框架在运行中学习的。为了展示这种多任务学习方法对自动作文等级的功效,我们收集了四组作文48份作文的凝视行为,并学习了7 000多篇作文的凝视行为。我们利用所学的凝视行为,我们可以在统计上大大改进我们所观察数据的论文集最新系统的业绩。我们还从统计上显著改进了另外四套作文,有大约6 000篇作文,我们没有关于凝视行为的数据。我们的方法确定,学习凝视行为可以改进自动作文等级。