Vasco Martins


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2025

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The iRead4Skills Intelligent Complexity Analyzer
Wafa Aissa | Raquel Amaro | David Antunes | Thibault Bañeras-Roux | Jorge Baptista | Alejandro Catala | Luís Correia | Thomas François | Marcos Garcia | Mario Izquierdo-Álvarez | Nuno Mamede | Vasco Martins | Miguel Neves | Eugénio Ribeiro | Sandra Rodriguez Rey | Elodie Vanzeveren
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present the iRead4Skills Intelligent Complexity Analyzer, an open-access platform specifically designed to assist educators and content developers in addressing the needs of low-literacy adults by analyzing and diagnosing text complexity. This multilingual system integrates a range of Natural Language Processing (NLP) components to assess input texts along multiple levels of granularity and linguistic dimensions in Portuguese, Spanish, and French. It assigns four tailored difficulty levels using state-of-the-art models, and introduces four diagnostic yardsticks—textual structure, lexicon, syntax, and semantics—offering users actionable feedback on specific dimensions of textual complexity. Each component of the system is supported by experiments comparing alternative models on manually annotated data.