Applications of the Transformer Architecture in AI-Assisted English Reading Comprehension

TL;DR

This paper introduces an interpretable, fair transformer-based model for English reading comprehension that outperforms existing models and improves teacher trust in AI-assisted educational settings.

Contribution

The paper presents a unified pipeline with bias correction, attribution analysis, and visualization techniques, advancing interpretability and fairness in transformer models for reading comprehension.

Findings

Model significantly outperforms state-of-the-art accuracy and F1 scores.

In multi-week experiments, it increased teachers' trust and operability.

Achieves high prediction accuracy and fairness, matching or surpassing human evaluation.

Abstract

This paper studies interpretable and fair artificial intelligence architectures for understanding English reading. Introduced transformer-based models, integrating advanced attention mechanisms and gradient-based feature attribution. The model's lack of interpretability, reduction of algorithmic bias, and unreliable performance in learning environments are the current issues faced in natural language teaching. A unified technical pipeline has been constructed, including adversarial bias correction methods, token-level attribution analysis, and multi-head attention heatmap visualization. Experimental validation was conducted using a large-scale labeled English reading comprehension dataset, and the data partitioning scheme and parameter optimization procedures have been determined. The method significantly outperforms the state-of-the-art models for this task in terms of accuracy and macro-average F1 score; in some aspects, it even surpasses or closely matches the results of human evaluations. In multi-week user experiments, the explainable transformer improved teachers' trust and operability in feedback-based assessments within the scoring system. The proposed method aims to ensure high prediction accuracy and fairness for different learners. This indicates that it is a real-world educational application based on artificial intelligence with a focus on interpretation. Improve the user experience in AI-assisted reading comprehension systems, counteract biases, and enhance the details explained by transformers.