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CNN models for readability of Chinese texts

  • *Corresponding author: Le-Yin Wei

    *Corresponding author: Le-Yin Wei 
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  • Readability of Chinese texts considered in this paper is a multi-class classification problem with $ 12 $ grade classes corresponding to $ 6 $ grades in primary schools, $ 3 $ grades in middle schools, and $ 3 $ grades in high schools. A special property of this problem is the strong ambiguity in determining the grades. To overcome the difficulty, a measurement of readability assessment methods used empirically in practice is adjacent accuracy in addition to exact accuracy. In this paper we give mathematical definitions of these concepts in a learning theory framework and compare these two quantities in terms of the ambiguity level of texts. A deep learning algorithm is proposed for readability of Chinese texts, based on convolutional neural networks and a pre-trained BERT model for vector representations of Chinese characters. The proposed CNN model can extract sentence and text features by convolutions of sentence representations with filters and is efficient for readability assessment, which is demonstrated with some numerical experiments.

    Mathematics Subject Classification: Primary: 68T07, 68T50; Secondary: 68Q32.

    Citation:

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  • Figure 1.  One Filter Instance

    Figure 2.  Two Inputs & Two Filters

    Figure 3.  Accuracy Curve by Epoch Number

    Figure 4.  Confusion Matrix

    Figure 5.  Scatter Plot

    Table 1.  Number of Texts in Each Grade

    Grade 1 2 3 4 5 6 7 8 9 10 11 12 Total
    Texts 235 320 386 321 281 252 145 58 134 86 26 109 2353
     | Show Table
    DownLoad: CSV

    Table 2.  Empirical accuracies of various models

    Model Vec2Read[12] Tseng et al.[12] Basic Multi-Channel Top-$ k $ Fused
    $ \hat{{\mathcal A}} $ 29.18 29.00 43.9 44.8 45.6 48.6
    $ \hat{{\mathcal A}}_{\mathcal C} $ 69.70 67.05 83.7 84.3 83.7 88.0
     | Show Table
    DownLoad: CSV
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