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The use of language models for generating lyrics and poetry has received anincreased interest in the last few years. They pose a unique challenge relativeto standard natural language problems, as their ultimate purpose is reative,notions of accuracy and reproducibility are secondary to notions of lyricism,structure, and diversity. In this creative setting, traditional quantitativemeasures for natural language problems, such as BLEU scores, prove inadequate:a high-scoring model may either fail to produce output respecting the desiredstructure (e.g. song verses), be a terribly boring creative companion, or both.In this work we propose a mechanism for combining two separately trainedlanguage models into a framework that is able to produce output respecting thedesired song structure, while providing a richness and diversity of vocabularythat renders it more creatively appealing.

Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation