"To me the meanest flower that blows can give
Thoughts that do often lie too deep for tears."
- Intimations of Immortality
Learning to generate poetry in the style of the poet can make models style experts, but humans who create imitative works take a more general approach that incorporates knowledge outside the poet's style. Instead of learning from a large corpus of one poet's works, can machines imitate deep style using only one example of her work? To explore generating poetic variations, I wrote eight poems that imitated the structure of eight poets, and used them to fine tune a transformer model that has seen only one poem by each author. The poems presented show structures borrowing from the human imitation in addition to prompted content of the original, suggesting the model has learned aspects of how humans write variations on content by imitating style. Audience evaluation reveals an ability for machine-generated text to reproduce the nuance of the original text as well as the human variation, despite being less expressive.