Automatic scoring method for essays based on label embedding
Currently,the automatic scoring methods for essays often use large pre-trained models to obtain semantic features,and the performance of such methods is not satisfactory because the pre-trained corpus does not match the domain features of essays,and the extraction of features for long essays is not effective.The paper proposes a label embedding-based automatic scoring method for essays,using an improved BiL-STM network and BERT model to extract domain features and abstract features of essays,while using a ga-ting mechanism to adjust the influence of both on essay scoring,and finally automatic scoring of essays through feature fusion.The experiment results show that the proposed model performs significantly better on the essay auto-scoring data set of the Kaggle ASAP competition,with an average QWK value of 81.22％,verifying the effectiveness of the label embedding approach in the essay auto-scoring task.
computer application techniquespre-trained embeddinglabel embeddingfeature fusionnatural language processing
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