ACL 2023short1 citations
The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation
Edwin Agnew, Michelle Qiu, Lily Zhu, Sam Wiseman, Cynthia Rudin
Abstract
We consider the automated generation of sonnets, a poetic form constrained according to meter, rhyme scheme, and length. Sonnets generally also use rhetorical figures, expressive language, and a consistent theme or narrative. Our constrained decoding approach allows for the generation of sonnets within preset poetic constraints, while using a relatively modest neural backbone. Human evaluation confirms that our approach produces Shakespearean sonnets that resemble human-authored sonnets, and which adhere to the genre’s defined constraints and contain lyrical language and literary devices.
BibTeX
@inproceedings{agnew-etal-2023-mechanical,
title = "The Mechanical Bard: An Interpretable Machine Learning Approach to {S}hakespearean Sonnet Generation",
author = "Agnew, Edwin and
Qiu, Michelle and
Zhu, Lily and
Wiseman, Sam and
Rudin, Cynthia",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.140/",
doi = "10.18653/v1/2023.acl-short.140",
pages = "1627--1638"
}