Text readability assessment involves categorizing texts based on readers' comprehension levels. Hybrid automatic readability assessment (ARA) models, combining deep and linguistic features, have recently attracted rising attention due to their impressive performance. However, existing hybrid ARA models generally ignore the specific-intrinsic information of deep and linguistic representations, and cannot fully explore their common-intrinsic information. In this paper, we introduce a self-supervised collaborative information bottleneck (SCIB) module for ARA to address these issues. Specifically, we collaboratively consider both specific-intrinsic and common-intrinsic information of the linguistic representation and various levels of deep representations including the document-, sentence- and word-level deep representations, and yield their refined representations via a self-supervised information bottleneck scheme. Extensive experiments are conducted on four English and two Chinese corpora to demonstrate the effectiveness of the proposed model. Experimental results show that the proposed model outperforms state-of-the-art models in terms of four important evaluation metrics, and the suggested SCIB module can effectively capture the specific- and common-intrinsic information.

The hybrid automatic readability assessment (ARA) models that combine deep and linguistic features have recently received rising attention due to their impressive performance. However, the utilization of linguistic features is not fully realized, as ARA models frequently concentrate excessively on numerical values of these features, neglecting valuable structural information embedded within them. This leads to limited contribution of linguistic features in these hybrid ARA models, and in some cases, it may even result in counterproductive outcomes. In this paper, we propose a novel hybrid ARA model named InterpretARA through introducing a linguistic interpreter to better comprehend the structural information contained in linguistic features, and leveraging the contrastive learning that enables the model to understand relative difficulty relationships among texts and thus enhances deep representations. Both document-level and segment-level deep representations are extracted and used for the readability assessment. A series of experiments are conducted over four English corpora and one Chinese corpus to demonstrate the effectiveness of the proposed model. Experimental results show that InterpretARA outperforms state-of-the-art models in most corpora, and the introduced linguistic interpreter can provide more useful information than existing ways for ARA.

Acquiring vocabulary is important when studying English, as it assists in listening, speaking, reading, and writing. In this paper, we develop an English webpage corpus (EWC) and create a word frequency list using web crawler technology. By comparing EWC word lists with the British National Corpus (BNC), we find that the BNC word frequency list possesses the feature of timeliness. We also explore primary school students’ English word recognition rates by comparing the word frequency lists of several corpora, including EWC, BNC, SUBTLEX-US, and Subtitle Corpus of Children’s BBC (CBBC). The results show that the word recognition rates for primary school children are relatively low in both general language and specific language register. Motivated by the experiment results, we finally propose some word-selection strategies for compiling English textbooks for Chinese primary school students.