Language independent optimization of text readability formulas with deep reinforcement learning

Abstract

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.