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        <title>Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders</title>
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            <title>Proceedings of the 13th International Conference on Natural Language Generation</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders
%A Nikolov, Nikola I.
%A Malmi, Eric
%A Northcutt, Curtis
%A Parisi, Loreto
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F nikolov-etal-2020-rapformer
%X 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.
%R 10.18653/v1/2020.inlg-1.42
%U https://aclanthology.org/2020.inlg-1.42
%U https://doi.org/10.18653/v1/2020.inlg-1.42
%P 360-373

Markdown (Informal)

[Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders](https://aclanthology.org/2020.inlg-1.42) (Nikolov et al., INLG 2020)

• Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders (Nikolov et al., INLG 2020)

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.