Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features

Yufei Tian, Nanyun Peng


Abstract
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.
Anthology ID:
2022.naacl-main.262
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3587–3597
Language:
URL:
https://aclanthology.org/2022.naacl-main.262
DOI:
10.18653/v1/2022.naacl-main.262
Bibkey:
Cite (ACL):
Yufei Tian and Nanyun Peng. 2022. Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3587–3597, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features (Tian & Peng, NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.262.pdf
Code
 pluslabnlp/sonnet-gen