Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Traffic Flow Prediction Based on ACO-BI-LSTM Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Label Embedding Based Scoring Method for Secondary School Essays

Authors : Chao Song, Ge Ren, YinZhong Song, JunJie Liu, Yong Yang

Published in: Artificial Intelligence in China

Publisher: Springer Nature Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Research on Helmet Detection Algorithm Based on Improved YOLOv5s
next chapter Research on Wheat Ears Detection Method Based on Improved YOLOv5
Literature
1.
Ghasemi, N., Aliannejadi, M., Hiemstra, D.: BERT for Target Apps Selection: Analyzing the Diversity and Performance of BERT in Unified Mobile Search (2021)
2.
Stefanick, M.L., Anderson, G.L., Margolis, K.L., et al.: Effects of conjugated equine estrogens on breast cancer and mammography screening in postmenopausal women with hysterectomy. JAMA 295(14), 1647 (2006)CrossRef
3.
Rodriguez, P.U., Jafari, A., Ormerod, C.M.: Language models and Automated Essay Scoring (2019)
4.
Chen, M., Li, X.: Relevance-based automated essay scoring via hierarchical recurrent model. In: 2018 International Conference on Asian Language Processing (IALP) (2018)
5.
Alikaniotis, D., Yannakoudakis, H., Rei, M.: Automatic text scoring using neural networks. arXiv e-prints (2016)
6.
Dong, F., Yue, Z., Jie, Y.: Attention-based recurrent convolutional neural network for automatic essay scoring. In: Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) (2017)
7.
Dong, F., Zhang, Y.: Automatic Features for Essay Scoring -An Empirical Study (2018)
8.
Taghipour, K., Ng, H.T.: A neural approach to automated essay scoring. The 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP 2016) (2016)
9.
Zhou, X., Yang, L., Fan, X., et al.: Self-training vs Pre-trained Embeddings for Automatic Essay Scoring (2021)
10.
Yin, W., Schütze, H., Xiang, B., et al.: ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. Computer Science (2015)
11.
Devlin, J., Chang, M.W., Lee, K., et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)
12.
Tay, Y., Phan, M.C., Tuan, L.A., et al.: SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring (2017)
13.
Ormerod, C.M., Malhotra, A., Jafari, A.: Automated Essay Scoring Using Efficient Transformer-Based Language Models (2021)
14.
Xia, L., Mc, A., Jyn, C.: SEDNN: shared and enhanced deep neural network model for cross-prompt automated essay scoring. Knowledge-Based Systems 210, 106491 (2020)
15.
16.
Liu, J., Yang, X., Zhao, L.: Automated Essay Scoring based on Two-Stage Learning (2019)
17.
Yi, T., Phan, M.C., Tuan, L.A., et al.: SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring (2017)
18.
Fei, D., Yue, Z.: Automatic features for essay scoring – an empirical study. In: Conference on Empirical Methods in Natural Language Processing (2016)
19.
Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention Is All You Need. arXiv. arXiv (2017)
20.
Page, E.B.: Grading essays by computer: progress report. In: Proceedings of the invitational Conference on Testing Problems (1967)
21.
Shen, D., Wang, G., Wang, W., et al.: On the Use of Word Embeddings Alone to Represent Natural Language Sequences (2018)
22.
Zupanc, K., Bosnic, Z.: Automated essay evaluation with semantic analysis. Knowledge-Based Systems 120(Mar.15), 118–132 (2017)
23.
Clark, K., Luong, M.T., Le, Q.V., et al.: ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (2020)
24.
Li, C., Lin, L., Mao, W., Xiong, L., Lin, Y.: An automated essay scoring model based on stacking method. In: 2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI), pp. 248–252 (2022)
Metadata
Title
Label Embedding Based Scoring Method for Secondary School Essays
Authors
Chao Song
Ge Ren
YinZhong Song
JunJie Liu
Yong Yang
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Artificial Intelligence in China

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

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

Copyright Year: 2023

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

Premium Partner

    Image Credits