Among creative applications for algorithms, writing lyrics and poetry has proved particularly challenging. The art of producing lyrics with a machine also differs greatly from other tasks in natural-language processing. The ultimate goal is to be creative rather than accurate, which can have a difficult-to-pin-down definition.
That didn’t stop two researchers at Google from trying to create an automated lyric-generation machine. They approached the task with two separate machine-learning models. They trained the first on the structure of a massive set of lyrics by extrapolating individual song lines into their parts of speech. They trained the other on words from 20 popular books, to build up a corpus of diverse vocabulary. Then the machine mashed it all together by pouring the vocabulary words into the structures of existing lyric lines.
The result, measured on a scale of word and line diversity (in other words, a low fraction of repeated words and lines), proved better than the output of a model trained purely on lyrics or purely on words. But the lyrics are still hilariously garbage. Behold:
Don’t settle for half the story.
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