@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",
    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.",
}

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.
Agnew, Edwin Qiu, Michelle Zhu, Lily Wiseman, Sam Rudin, Cynthia
The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation