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We developed an interactive workshop designed to illustrate the NLP and computational linguistics to Italian high schoolers.

This paper investigates the relationship between two complementary perspectives in the human assessment of sentence complexity and how they are modeled in a neural language model (NLM), highlighting how linguistic information encoded in representations changes when the model learns to predict complexity.

This work describes a self-supervised data augmentation approach used to improve learning models' performances when only a moderate amount of labeled data is available.