Research on Automated Scoring Method for HSK Essays with Hybrid Features
Juncang Rao, Yanshan He · 2024
Automated essay scoring (AES) has assisted or replaced tedious manual scoring in many occasions. However,research on the technology and application of automatic scoring for Chinese as a second language essays is still insufficient. In this paper, we extract interlanguage error features from the HSK (Chinese Proficiency Test) essay corpus, integrate these with word-level semantic features of a pre-training language model, and employ a multi-feature hybrid model to enhance the consistency and interpretability of essay scoring. Experiments show that the proposed model improves the scoring consistency by 6.77% and 5.53% compared to the baseline pre-training model and the pre-training model fusing traditional features, respectively. Meanwhile, the proposed model performs more consistently in scoring experiments on various topics, inter-language error features are negatively correlated with scores and can better assist the final decision of the model compared to traditional features, suggesting that inter-language error features are more effective for representing text information in the scoring task than traditional essay features.