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Erschienen in:
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2023 | OriginalPaper | Buchkapitel

Label Embedding Based Scoring Method for Secondary School Essays

verfasst von : Chao Song, Ge Ren, YinZhong Song, JunJie Liu, Yong Yang

Erschienen in: Artificial Intelligence in China

Verlag: Springer Nature Singapore

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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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Metadaten
Titel
Label Embedding Based Scoring Method for Secondary School Essays
verfasst von
Chao Song
Ge Ren
YinZhong Song
JunJie Liu
Yong Yang
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
Artificial Intelligence in China

Print ISBN: 978-981-9912-55-1

Electronic ISBN: 978-981-9912-56-8

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-981-99-1256-8_13

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