论文标题
使用基于源的论文评分的神经网络注意分数自动局部组件提取
Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring
论文作者
论文摘要
虽然自动化的论文评分（AES）可以大规模可靠地评分论文，但自动写作评估（AWE）还提供了形成性的反馈来指导论文修订。但是，神经AE通常不会为支持敬畏提供有用的功能表示。本文通过提取局部组件（TCS）来使用注意力层的中间输出来代表源文本中的证据，介绍了一种链接敬畏和神经AE的方法。我们使用基于功能的AES需要TC来评估性能。结果表明，无论是自动使用或手动构造的TC用于1）将论文表示为基于标题的特征，2）评分论文是可比的。
While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.
