快乐的是那些没有看到的评分：使用凝视行为的多任务学习方法
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
摘要：
读者的凝视行为有助于解决几个NLP任务，例如自动论文评分。然而，从读者收集凝视行为在时间和金钱方面是昂贵的。在本文中，我们提出了一种方法来提高自动论文评分使用凝视行为，这是学习在运行时间使用多任务学习框架。为了证明这种基于多任务学习的方法对自动作文评分的有效性，我们收集了4个作文集的48篇作文的凝视行为，并学习了其余作文的凝视行为，数量超过7000篇作文。使用学习到的凝视行为，我们可以在统计上显著改善具有凝视数据的文章集的最先进系统的性能。我们还实现了其他4个论文集，编号约6000篇论文，我们没有凝视行为数据的统计学显着改善。我们的方法建立了学习凝视行为提高自动作文评分。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
