Multiattentive Hierarchical Recurrent Neural Network Architecture for Multilingual Readability Assessment

Abstract

We present a multiattentive recurrent neural network architecture for automatic multilingual readability assessment. This architecture considers raw words as its main input, but internally captures text structure and informs its word attention process using other syntax- and morphology-related datapoints, known to be of great importance to readability. This is achieved by a multiattentive strategy that allows the neural network to focus on specific parts of a text for predicting its reading level. We conducted an exhaustive evaluation using datasets targeting multiple languages and prediction task types, to compare the proposed model with traditional, state-of-the-art and other neural network strategies.