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This study introduces an improved model for ideological and political education (IPE) text scoring, enhancing accuracy through advanced transformer and Bert models for better semantic similarity calculation.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Educational Technology

Background:

  • Accurate scoring of ideological and political education (IPE) texts is crucial for effective learning.
  • Existing short-text similarity models face challenges in capturing nuanced semantic meanings and polysemy.

Purpose of the Study:

  • To propose an improved short-text similarity calculation model for enhancing the accuracy of IPE text scoring.
  • To leverage transformer and Bert models to address limitations in current text representation and similarity calculation.

Main Methods:

  • The proposed model utilizes the Deep Structured Semantic Model (DSSM) as its foundational framework.
  • Incorporates the Bert model for robust text representation, effectively addressing the polysemy problem.
  • Employs a transformer encoding component for in-depth feature extraction and multi-level text information interaction.

Main Results:

  • The model achieves improved accuracy in calculating semantic similarity between short texts.
  • Enhanced text representation and feature extraction capabilities lead to more precise scoring outcomes.
  • Multi-level information interaction within the transformer encoding component refines similarity assessment.

Conclusions:

  • The developed transformer-based model significantly improves the accuracy of ideological and political education text scoring.
  • The integration of Bert and transformer components offers a powerful approach to semantic similarity calculation.
  • This model provides a valuable tool for objective and accurate assessment in educational contexts.