Deep-speare: A joint neural model of poetic language, meter and rhyme

ACL 2018

Deep-speare: A joint neural model of poetic language, meter and rhyme

Jan 28, 2021
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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)

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