Automatic Poetry Generation from Prosaic Text
Open accessAbstract
In the last few years, a number of successful approaches have emerged that are able to adequately model various aspects of natural language.In particular, language models based on neural networks have improved the state of the art with regard to predictive language modeling, while topic models are successful at capturing clear-cut, semantic dimensions.In this paper, we explore how these approaches can be adapted and combined to model the linguistic and literary aspects needed for poetry generation.The system is exclusively trained on standard, non-poetic text, and its output is constrained in order to confer a poetic character to the generated verse.The framework is applied to the generation of poems in both English and French, and is equally evaluated for both languages.Even though it only uses standard, non-poetic text as input, the system yields state of the art results for poetry generation.
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The poetry generation system used in Study 1 is a state-of-the-art, scientifically validated neural poetry generation system that has been shown to generate output comparable to human-written poetry.
“The algorithm used to generate novel poetry is a state of the art, scientifically validated, neural poetry generation system that has been shown to generate output comparable to human-written poetry33.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [33]
Evaluations of the quality of poems produced by this poetry generation system revealed that raters considered the poems to be comparable to works by well-established English poets and superior to previous poetry systems across dimensions of fluency, coherence, meaningfulness, and poeticism.
“Evaluations of the quality of poems produced by this poetry generation system revealed that raters considered the poems to be comparable to works by well-established English poets (W.H. Auden, E.E. Cummings, Philip Larkin, Sarojini Naidu, and Sylvia Plath), and superior to previous poetry systems (Hafez and Deep-speare) across dimensions of fluency, coherence, meaningfulness, and poeticism.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [33]
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Research works printed but not linked: 1
- Peter D Turney and Patrick Pantel. 2010. From fre- quency to meaning: Vector space models of se- mantics. Journal of artificial intelligence research, 37:141-188.
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Self-citation
- Authors
- Van de Cruys, T.
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 2471-2480
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3034807061
- Cited by
- 50
- References
- 30
- Citations by year
- 2025: 2 · 2024: 10 · 2023: 16 · 2022: 7 · 2021: 12 · 2020: 3
- FWCI
- 3.63
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Nvidia
- Agence Nationale de la Recherche (ANR-19-CE23-0025, ANR‐19)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- automatic poetry generation, neural language models, topic modeling, poetic style, English poetry, French poetry
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