Abstract
Automatic essay scoring techniques can automatically evaluate and score essays, and they have become one of the hot issues in the application of natural language processing techniques in education. Current automatic essay scoring methods often use large pre-trained models to obtain semantic features, which do not perform well in the field of automatic essay scoring because the training corpus does not match the content domain of the essay, and the extraction of features for long essays is not effective. We propose a label embedding-based method for scoring secondary school essays, using a modified bidirectional long- and short-term memory network and a BERT model to extract domain features and abstract features of essays, while using a gating mechanism to adjust the influence of both types of features on essay scoring, and finally automatic scoring of essays through feature fusion. The experimental results show that the proposed model performs significantly better on the essay auto-scoring dataset of the Kaggle ASAP competition, with an average QWK value of 81.22%, which verifies the effectiveness of the proposed algorithm in the essay auto-scoring task.
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Acknowledgment
This work was supported by XinJiang Uygur Autonomous Region Natural Science Foundation Project (No. 2021D01B72, 2022D01A99), the Natural Science Foundation of China (No.62167008, 62066044), and National Natural Science Foundation young investigator grant program (No. 62006130).
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Song, C., Ren, G., Song, Y., Liu, J., Yang, Y. (2023). Label Embedding Based Scoring Method for Secondary School Essays. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. AIC 2022. Lecture Notes in Electrical Engineering, vol 871. Springer, Singapore. https://doi.org/10.1007/978-981-99-1256-8_13
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DOI: https://doi.org/10.1007/978-981-99-1256-8_13
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