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Article published In:
Information Design Journal
Vol. 28:1 (2023) ► pp.33–52

Language independent optimization of text readability formulas with deep reinforcement learning

 | University of Kansas
ORCID logo | Institute for Humanities and Cultural Studies
Readability formulas are used to assess the level of difficulty of a text. These language dependent formulas are introduced with pre-defined parameters. Deep reinforcement learning models can be used for parameter optimization. In this article we argue that an Actor-Critic based model can be used to optimize the parameters in the readability formulas. Furthermore, a selection model is proposed for selecting the most suitable formula to assess the readability of the input text. English and Persian data sets are used for both training and testing. The experimental results of the parameter optimization model show that, on average, the F-score of the model for English increases from 24.7% in the baseline to 38.8%, and for Persian from 23.5% to 47.7%. The proposed algorithm selection model further improves the parameter optimization model to 65.5% based on F-score for both English and Persian.
Keywords: text readability, deep reinforcement learning, parameter optimization
Article outline
  • 1.Introduction
  • 2.Related Works
  • 3.Proposed method
    • 3.1Reinforcement learning
      • 3.1.1State space
      • 3.1.2Action space
      • 3.1.3Rewarding
    • 3.2Parameter optimization
    • 3.3Algorithm selection
    • 3.4Model training
  • 4.Experimental results
    • 4.1Dataset
      • 4.1.1English dataset
      • 4.1.2Persian dataset
    • 4.2Setup of experiments
    • 4.3Results and discussion
  • 5.Conclusion and future work
  • Notes
  • References
Published online: 25 May 2023
DOI logo
https://doi.org/10.1075/idj.22015.had
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