A Prompt-Independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing

Yupei Wang, Renfen Hu

Abstract: With the increasing popularity of learning Chinese as a second language (L2), the development of an automated essay scoring (AES) method specially for Chinese L2 essays has become an important task. To build a robust model that could easily adapt to prompt changes, we propose 90 linguistic features with consideration of both language complexity and correctness, and introduce the Ordinal Logistic Regression model that explicitly combines these linguistic features and low-level textual representations. Our model obtains a high QWK of 0.714, a low RMSE of 1.516 and a considerable Pearson correlation of 0.734. With a simple linear model, we further analyze the contribution of the linguistic features to score prediction, revealing the model’s interpretability and its potential to give writing feedback to users. This work provides insights and establishes a solid baseline for Chinese L2 AES studies.