Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features
Yufei Tian, Nanyun Peng · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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.1