@inproceedings{tian-peng-2022-zero,
title = "Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features",
author = "Tian, Yufei and
Peng, Nanyun",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.262/",
doi = "10.18653/v1/2022.naacl-main.262",
pages = "3587--3597",
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."
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%0 Conference Proceedings
%T Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features
%A Tian, Yufei
%A Peng, Nanyun
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F tian-peng-2022-zero
%X 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.
%R 10.18653/v1/2022.naacl-main.262
%U https://aclanthology.org/2022.naacl-main.262/
%U https://doi.org/10.18653/v1/2022.naacl-main.262
%P 3587-3597
Markdown (Informal)
[Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features](https://aclanthology.org/2022.naacl-main.262/) (Tian & Peng, NAACL 2022)
ACL