Supervised and Unsupervised Neural Approaches to Text Readability

Abstract

We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labelled readability datasets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.

arXiv comment39 pages, published in Computational Linguistic Journal

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Cited by in corpus (7)

• Linguistic Features for Readability Assessment
• Optimizing Readability Using Genetic Algorithms
• TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions
• A Simple Post-Processing Technique for Improving Readability Assessment of Texts using Word Mover's Distance
• Age Recommendation from Texts and Sentences for Children
• BERT Embeddings for Automatic Readability Assessment
• Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features