Neural Poetry: Learning to Generate Poems using Syllables

Andrea Zugarini (1,2), Stefano Melacci (2), Marco Maggini (2)

(1) DINFO, University of Florence
(2) DIISM, University of Siena

Abstract

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.

Bibtex

@inproceedings{zugarini2019neural,
title={Neural Poetry: Learning to Generate Poems Using Syllables},
author={Zugarini, Andrea and Melacci, Stefano and Maggini, Marco},
booktitle={International Conference on Artificial Neural Networks},
pages={313–325},
year={2019},
organization={Springer},
doi={10.1007/978-3-030-30490-4_26},
url={https://doi.org/10.1007/978-3-030-30490-4_26}
}

Preprint version: here

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