Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders
Nikola I. Nikolov, Eric Malmi, Curtis G. Northcutt, Loreto Parisi · 2020
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 Turingtest-like experiment reveals that RAPFORMER fools human lyrics experts 25% of the time. 1