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Teaching Practices to Enhance English Reading Comprehension Using Natural Language Processing Technology

,  and   
Mar 19, 2025

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Figure 1.

Model structure
Model structure

Figure 2.

Effect after adding vector comparator
Effect after adding vector comparator

Figure 3.

Eigenvector heat diagram
Eigenvector heat diagram

Figure 4.

Accuracy and support information
Accuracy and support information

English reading score

Class Experimental class Control class
Test time Before experiment After experiment Before experiment After experiment
Mean 24.12 28.58 25.54 25.82
Case number 50 50 50 50
Standard deviation 6.178 4.792 5.598 5.536
T −9.911 −1.619
P 0.002 0.15

Contrast of the baseline model

Model RACE-1 (%) RACE-2 (%)
Bert 73.61 80.71
Bert+LUA 72.16 78.97
Bert+TLUA 82.38 80.01
Bert+FRA 83.8 80.96
Bert+Key words+Dependency syntax 83.15 81.46
Bert+Key words 73.34 70.42
Bert+Dependency syntax 68.71 76.55
Bert+Wordset+Dependency syntax 61.41 68.59
Bert+Key words+Dependency syntax 86.33 83.85

Performance contrast

Model RACE-1 (%) RACE-2 (%)
Richardson et al. 69.93 64.19
Wang et al 74.93 70.93
Li et al 74.3 72.25
Attentive Reader 46.59 41.23
Neural Reasoner 46.71 46.4
Parallel-Hierarchical 74.68 70.38
Reading Strategies 82.07 81.57
Bert 74.74 80
Model of this article 88.36 85.05
Language:
English