  Motivated by the recent progresses on machine learning-based models that
learn artistic styles, in this paper we focus on the problem of poem
generation. This is a challenging task in which the machine has to capture the
linguistic features that strongly characterize a certain poet, as well as the
semantics of the poet's production, that are influenced by his personal
experiences and by his literary background. Since poetry is constructed using
syllables, that regulate the form and structure of poems, we propose a
syllable-based neural language model, and we describe a poem generation
mechanism that is designed around the poet style, automatically selecting the
most representative generations. The poetic work of a target author is usually
not enough to successfully train modern deep neural networks, so we propose a
multi-stage procedure that exploits non-poetic works of the same author, and
also other publicly available huge corpora to learn syntax and grammar of the
target language. We focus on the Italian poet Dante Alighieri, widely famous
for his Divine Comedy. A quantitative and qualitative experimental analysis of
the generated tercets is reported, where we included expert judges with strong
background in humanistic studies. The generated tercets are frequently
considered to be real by a generic population of judges, with relative
difference of 56.25\% with respect to the ones really authored by Dante, and
expert judges perceived Dante's style and rhymes in the generated text.
