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Improving Automated Essay Scoring by Prompt Prediction and Matching.

Jingbo Sun1, Tianbao Song2, Jihua Song1

  • 1School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China.

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|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a prompt feature fusion method for automated essay scoring, enhancing natural language processing models. Multi-task learning with auxiliary tasks significantly improves scoring accuracy on the HSK dataset.

Keywords:
automated essay scoringhierarchical structure modelmulti-task learningnatural language processingpre-trained language model

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Area of Science:

  • Natural Language Processing
  • Educational Technology
  • Artificial Intelligence

Background:

  • Automated essay scoring (AES) is a key application of NLP in education.
  • Pre-trained models are increasingly used for AES, but prompt feature extraction needs improvement.
  • Current methods lack sufficient focus on optimizing prompt features for fine-tuning.

Purpose of the Study:

  • To develop an effective prompt feature fusion method for AES.
  • To enhance feature extraction from pre-trained encoders for better essay scoring.
  • To investigate the impact of multi-task learning on AES performance.

Main Methods:

  • A novel prompt feature fusion method was created for fine-tuning.
  • Multi-task learning was employed with two auxiliary tasks: prompt prediction and prompt matching.
  • The NEZHA pre-trained encoder was utilized and evaluated.

Main Results:

  • Both auxiliary tasks individually improved model performance in AES.
  • The combination of auxiliary tasks with the NEZHA encoder yielded the best results.
  • Quadratic Weighted Kappa improved by 2.5% and Pearson's Correlation Coefficient by 2% on average.

Conclusions:

  • Multi-task learning and prompt feature fusion are effective strategies for enhancing AES.
  • The proposed method significantly boosts the performance of pre-trained models in educational applications.
  • This research offers a promising direction for more accurate automated essay evaluation.