In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We assess the quality of generated poems using crowd and expert judgements. The stress and rhyme models perform very well, as generated poems are largely indistinguishable from human-written poems. Expert evaluation, however, reveals that a vanilla language model captures meter implicitly, and that machine-generated poems still underperform in terms of readability and emotion. Our research shows the importance expert evaluation for poetry generation, and that future research should look beyond rhyme/meter and focus on poetic language.
@article{arxiv.1807.03491,
title = {Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme},
author = {Jey Han Lau and Trevor Cohn and Timothy Baldwin and Julian Brooke and Adam Hammond},
journal= {arXiv preprint arXiv:1807.03491},
year = {2018}
}