Multiattentive Recurrent Neural Network Architecture for Multilingual Readability Assessment
Year: 2019
Type: article
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 data sets targeting multiple languages and prediction task types, to compare the proposed model with traditional, state-of-the-art, and other neural network strategies.
DOI: 10.1162/tacl_a_00278
Authors: Ion Madrazo Azpiazu, Maria Soledad Pera
Institution: Boise State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2965628884