  Poetry generation, and creative language generation in general, usually
suffers from the lack of large training data. In this paper, we present a novel
framework to generate sonnets that does not require training on poems. We
design a hierarchical framework which plans the poem sketch before decoding.
Specifically, a content planning module is trained on non-poetic texts to
obtain discourse-level coherence; then a rhyme module generates rhyme words and
a polishing module introduces imagery and similes for aesthetics purposes.
Finally, we design a constrained decoding algorithm to impose the
meter-and-rhyme constraint of the generated sonnets. Automatic and human
evaluation show that our multi-stage approach without training on poem corpora
generates more coherent, poetic, and creative sonnets than several strong
baselines.
