论文标题
那些不看不见的人是快乐的：一种多任务学习方法，以凝视行为来评分论文
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
论文作者
论文摘要
读者的凝视行为有助于解决多个NLP任务，例如自动论文分级。但是，从时间和金钱方面，从读者那里收集凝视行为是昂贵的。在本文中，我们提出了一种使用凝视行为来改善自动论文分级的方法，这是在运行时间使用多任务学习框架学习的。为了证明这种基于多任务学习的自动论文分级方法的功效，我们收集了48篇论文集的48篇论文的凝视行为，并在其余文章中学习目光的行为，编号超过7000篇论文。使用学习的凝视行为，我们可以在拥有凝视数据的论文集的最新系统上实现统计学上的显着改善。我们还为其他4篇论文集实现了统计学上的显着改进，数量约为6000篇论文，我们没有可用的凝视行为数据。我们的方法确定学习目光行为可以改善自动论文的评分。
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.
