Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders
Nikola I. Nikolov, Eric Malmi, Curtis Northcutt, Loreto Parisi
Abstract
The ability to combine symbols to generate language is a defining characteristic of human intelligence, particularly in the context of artistic story-telling through lyrics. We develop a method for synthesizing a rap verse based on the content of any text (e.g., a news article), or for augmenting pre-existing rap lyrics. Our method, called Rapformer, is based on training a Transformer-based denoising autoencoder to reconstruct rap lyrics from content words extracted from the lyrics, trying to preserve the essential meaning, while matching the target style. Rapformer features a novel BERT-based paraphrasing scheme for rhyme enhancement which increases the average rhyme density of output lyrics by 10%. Experimental results on three diverse input domains show that Rapformer is capable of generating technically fluent verses that offer a good trade-off between content preservation and style transfer. Furthermore, a Turing-test-like experiment reveals that Rapformer fools human lyrics experts 25% of the time.- Anthology ID:
- 2020.inlg-1.42
- Volume:
- Proceedings of the 13th International Conference on Natural Language Generation
- Month:
- December
- Year:
- 2020
- Address:
- Dublin, Ireland
- Editors:
- Brian Davis, Yvette Graham, John Kelleher, Yaji Sripada
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 360–373
- Language:
- URL:
- https://aclanthology.org/2020.inlg-1.42
- DOI:
- 10.18653/v1/2020.inlg-1.42
- Cite (ACL):
- Nikola I. Nikolov, Eric Malmi, Curtis Northcutt, and Loreto Parisi. 2020. Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders. In Proceedings of the 13th International Conference on Natural Language Generation, pages 360–373, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders (Nikolov et al., INLG 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.inlg-1.42.pdf