  Poetry Generation involves teaching systems to automatically generate text
that resembles poetic work. A deep learning system can learn to generate poetry
on its own by training on a corpus of poems and modeling the particular style
of language. In this paper, we propose taking an approach that fine-tunes
GPT-2, a pre-trained language model, to our downstream task of poetry
generation. We extend prior work on poetry generation by introducing creative
elements. Specifically, we generate poems that express emotion and elicit the
same in readers, and poems that use the language of dreams---called dream
poetry. We are able to produce poems that correctly elicit the emotions of
sadness and joy 87.5 and 85 percent, respectively, of the time. We produce
dreamlike poetry by training on a corpus of texts that describe dreams. Poems
from this model are shown to capture elements of dream poetry with scores of no
less than 3.2 on the Likert scale. We perform crowdsourced human-evaluation for
all our poems. We also make use of the Coh-Metrix tool, outlining metrics we
use to gauge the quality of text generated.
