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In this paper, we present design and construction of the first Italian corpus for automatic and semi-automatic text simplification. In line with current approaches, we propose a new annotation scheme specifically conceived to identify the typology of changes an original sentence undergoes when it is manually simplified. Such a scheme has been applied to two aligned Italian corpora, containing original texts with corresponding simplified versions, selected as representative of two different manual simplification strategies and addressing different target reader populations. Each corpus was annotated with the operations foreseen in the annotation scheme, covering different levels of linguistic description. Annotation results were analysed with the final aim of capturing peculiarities and differences of the different simplification strategies pursued in the two corpora.

In this paper, we present a crowdsourcingbased approach to model the human perception of sentence complexity. We collect a large corpus of sentences rated with judgments of complexity for two typologically-different languages, Italian and English. We test our approach in two experimental scenarios aimed to investigate the contribution of a wide set of lexical, morpho-syntactic and syntactic phenomena in predicting i) the degree of agreement among annotators independently from the assigned judgment and ii) the perception of sentence complexity.

In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems. We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks. We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.

In this paper we present PaCCSS-IT, a Parallel Corpus of Complex-Simple Sentences for ITalian. To build the resource we develop a new method for automatically acquiring a corpus of complex-simple paired sentences able to intercept structural transformations and particularly suitable for text simplification. The method requires a wide amount of texts that can be easily extracted from the web making it suitable also for less-resourced languages. We test it on the Italian language making available the biggest Italian corpus for automatic text simplification.