Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model
Table 3. Datasets information.
['[BOLD] Dataset', '[BOLD] # classes', '[BOLD] # texts (balanced)', '[BOLD] # texts/class', '[BOLD] # words/text']
Three datasets are used to evaluate three different aspects of the proposed model. Weebit dataset is gathered from articles in the Weekly Reader magazine and the BBC-Bitesize website, which is targeted at readers of various ages. Weebit dataset comprises texts from five different readability levels arranged by age (8-9, 9-10, 10-12, 11-14, 14-16). The total number of texts in the dataset is over ten thousand texts. Nonetheless, due to the significant imbalance in the dataset, some texts are randomly removed from some classes. Prior to this study, distinct models have been used to assess the readability of English texts for second language readers. Since the DRL model eliminates the need for specific feature engineering for different types of text readability assessment, the proposed model can be applied to second language datasets without any modifications. This dataset contains texts from the reading section of Cambridge English Exams, which is targeted for students at five readability levels (A2 to C2) of the Common European Framework of Reference (CEFR). This dataset contains 331 texts, which makes it a small dataset in comparison to the Weebit dataset. The automated feature extraction ability of the proposed model has also given the model the ability to be readily applied to other languages. As GloVe and language models are language-specific, the only necessary change is the use of the GloVe and frequency language model of the target language. These features are readily and freely available on the internet for many languages. This dataset includes near four thousand texts, which are split into three balanced classes.