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Research on Automatic Essay Scoring of Composition Based on CNN and OR

机译：基于CNN和OR的作文自动作文评分研究

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摘要

Manual scoring may be affected by unconscious factors such as subjective judgment of the reviewer, which may lead to the deviation of scores. Establishing an objective, systematic automatic comment system is of great significance to solve this problem. Most of the existing automatic scoring systems are based on a single neural network or based on feature extraction. The accuracy of the formers need to be improved, and the latter require a lot of time to manually extract features. After exploring and analyzing convolutional neural networks (CNN) and ordinal regression (OR), we propose an automatic essay scoring approach based on a combination of CNN and OR for the characteristics of automatic scoring mechanism. Through the deep learning framework Keras to realize the designing, the experimental results demonstrate that the proposed model has a great improvement on the accuracy and efficiency of the automatic essay scoring than existing methods.
机译：手动评分可能会受到无意识因素（例如审阅者的主观判断）的影响，这可能会导致分数出现偏差。建立客观，系统的自动评论系统对解决这一问题具有重要意义。现有的大多数自动评分系统都基于单个神经网络或基于特征提取。前者的准确性需要提高，而后者则需要大量时间才能手动提取特征。在对卷积神经网络（CNN）和序数回归（OR）进行探索和分析之后，针对自动评分机制的特点，提出了一种基于CNN和OR的自动论文评分方法。通过深度学习框架Keras来实现设计，实验结果表明，与现有方法相比，所提模型对自动作文评分的准确性和效率有很大的提高。

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