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.- Anthology ID:
- 2023.acl-short.140
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1627–1638
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.140
- DOI:
- Cite (ACL):
- Edwin Agnew, Michelle Qiu, Lily Zhu, Sam Wiseman, and Cynthia Rudin. 2023. The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1627–1638, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation (Agnew et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.acl-short.140.pdf