This paper demonstrates the effectiveness of a Long Short-Term Memory language model in our initial efforts to generate unconstrained rap lyrics. The goal of this model is to generate lyrics that are similar in style to that of a given rapper, but not identical to existing lyrics: this is the task of ghostwriting. Unlike previous work, which defines explicit templates for lyric generation, our model defines its own rhyme scheme, line length, and verse length. Our experiments show that a Long Short-Term Memory language model produces better "ghostwritten" lyrics than a baseline model.
Potash et al. REF applied long short-term memory network in generating lyrics that were similar in style to that of a certain rapper, and they presented computational and quantitative evaluation methods.
GhostWriter: Using an LSTM for Automatic Rap Lyric Generation
EMNLP
Computer Science