  In the last few years, pre-trained neural architectures have provided
impressive improvements across several NLP tasks. Still, generative language
models are available mainly for English. We develop GePpeTto, the first
generative language model for Italian, built using the GPT-2 architecture. We
provide a thorough analysis of GePpeTto's quality by means of both an automatic
and a human-based evaluation. The automatic assessment consists in (i)
calculating perplexity across different genres and (ii) a profiling analysis
over GePpeTto's writing characteristics. We find that GePpeTto's production is
a sort of bonsai version of human production, with shorter but yet complex
sentences. Human evaluation is performed over a sentence completion task, where
GePpeTto's output is judged as natural more often than not, and much closer to
the original human texts than to a simpler language model which we take as
baseline.
