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Lyric generation is a popular sub-field of natural language generation thathas seen growth in recent years. Pop lyrics are of unique interest due to thegenre's unique style and content, in addition to the high level ofcollaboration that goes on behind the scenes in the professional popsongwriting process. In this paper, we present a collaborative line-level lyricgeneration system that utilizes transfer-learning via the T5 transformer model,which, till date, has not been used to generate pop lyrics. By working andcommunicating directly with professional songwriters, we develop a model thatis able to learn lyrical and stylistic tasks like rhyming, matching line beatrequirements, and ending lines with specific target words. Our approachcompares favorably to existing methods for multiple datasets and yieldspositive results from our online studies and interviews with industrysongwriters.

Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning