Hierarchical Ranking Neural Network for Long Document Readability Assessment
This work addresses readability assessment for long texts, which is important for applications like education and content adaptation, but it is incremental as it builds on existing deep learning approaches by incorporating length and ordinal considerations.
The paper tackled the problem of readability assessment for long documents by proposing a bidirectional mechanism that predicts sentence-level readability and uses a pairwise sorting algorithm to model ordinal relationships between labels. The model achieved competitive performance and outperformed baseline models on Chinese and English datasets.
Readability assessment aims to evaluate the reading difficulty of a text. In recent years, while deep learning technology has been gradually applied to readability assessment, most approaches fail to consider either the length of the text or the ordinal relationship of readability labels. This paper proposes a bidirectional readability assessment mechanism that captures contextual information to identify regions with rich semantic information in the text, thereby predicting the readability level of individual sentences. These sentence-level labels are then used to assist in predicting the overall readability level of the document. Additionally, a pairwise sorting algorithm is introduced to model the ordinal relationship between readability levels through label subtraction. Experimental results on Chinese and English datasets demonstrate that the proposed model achieves competitive performance and outperforms other baseline models.