  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.
