Abstract: In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We as-sess 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, re-veals 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.
Authors: Jey Han Lau, Trevor Cohn, Timothy Baldwin, Julian Brooke, Adam Hammond (IBM Research, The University of Melbourne, University of Toronto)