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

  • Zhiyun Chen*
  • , Yuxin Zhou
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages13-18
Number of pages6
ISBN (Electronic)9781728108315
DOIs
StatePublished - May 2019
Event2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019 - Chengdu, China
Duration: 25 May 201928 May 2019

Publication series

Name2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019

Conference

Conference2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019
Country/TerritoryChina
CityChengdu
Period25/05/1928/05/19

Keywords

  • Keras
  • automatic essay scoring
  • convolutional neural networks
  • deep learning
  • ordinal regression

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