In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.
Schicchi D., Pilato G., Lo Bosco G. (2020). Deep neural attention-based model for the evaluation of italian sentences complexity. In Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020 (pp. 253-256). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSC.2020.00053].
Deep neural attention-based model for the evaluation of italian sentences complexity
Schicchi D.;Pilato G.;Lo Bosco G.
2020-01-01
Abstract
In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.
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