Hey AI Can You Grade My Essay?: Automatic Essay Grading

TL;DR

This paper introduces a novel collaborative and transfer learning model for automatic essay grading that outperforms existing methods, achieving an accuracy of 85.50% by separately analyzing grammatical structure and essay content.

Contribution

The work proposes a multi-network approach combining collaborative and transfer learning to improve essay grading accuracy over single-network models.

Findings

Achieved 85.50% accuracy in essay grading

Outperformed state-of-the-art models

Demonstrated effectiveness of multi-network transfer learning

Abstract

Automatic essay grading (AEG) has attracted the the attention of the NLP community because of its applications to several educational applications, such as scoring essays, short answers, etc. AEG systems can save significant time and money when grading essays. In the existing works, the essays are graded where a single network is responsible for the whole process, which may be ineffective because a single network may not be able to learn all the features of a human-written essay. In this work, we have introduced a new model that outperforms the state-of-the-art models in the field of AEG. We have used the concept of collaborative and transfer learning, where one network will be responsible for checking the grammatical and structural features of the sentences of an essay while another network is responsible for scoring the overall idea present in the essay. These learnings are transferred to another network to score the essay. We also compared the performances of the different models mentioned in our work, and our proposed model has shown the highest accuracy of 85.50%.