Conditional Rap Lyrics Generation with Denoising Autoencoders
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We develop a method for automatically synthesizing a rap verse given an input text written in another form, such as a summary of a news article. Our approach is to train a Transformer-based denoising autoencoder to reconstruct rap lyrics from content words. We study three different approaches for automatically stripping content words that convey the essential meaning of the lyrics. Moreover, we propose a BERT-based paraphrasing scheme for rhyme enhancement and show that it increases the average rhyme density of the lyrics by 10 Experimental results on three diverse input domains – existing rap lyrics, news, and movie plot summaries – show that our method is capable of generating coherent and technically fluent rap verses that preserve the input content words. Human evaluation demonstrates that our approach gives a good trade-off between content preservation and style transfer compared to a strong information retrieval baseline.
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