Automated Chinese Essay Scoring using Pre-Trained Language Models

Abstract

Automated Essay Scoring (AES) aims to assign a proper score to an essay written by a given prompt, which is a significant application of Natural Language Processing (NLP) in the education area. In this work, we focus on solving the Chinese AES problem by Pre-trained Language Models (PLMs) including state-of-the-art PLMs BERT and ERNIE. A Chinese essay dataset has been built up in this work, by which we conduct extensive AES experiments. Our PLMs-based AES models acquire 68.70% in Quadratic Weighted Kappa (QWK), which outperform classic feature-based linear regression AES model. The results show that our methods effectively alleviate the dependence on manual features and improve the portability of AES models. Furthermore, we acquire well-performed AES models with a limited scale of the dataset, which solves the lack of datasets in Chinese AES.