  Persian Poetry has consistently expressed its philosophy, wisdom, speech, and
rationale on the basis of its couplets, making it an enigmatic language on its
own to both native and non-native speakers. Nevertheless, the notice able gap
between Persian prose and poem has left the two pieces of literature
medium-less. Having curated a parallel corpus of prose and their equivalent
poems, we introduce a novel Neural Machine Translation (NMT) approach to
translate prose to ancient Persian poetry using transformer-based Language
Models in an extremely low-resource setting. More specifically, we trained a
Transformer model from scratch to obtain initial translations and pretrained
different variations of BERT to obtain final translations. To address the
challenge of using masked language modelling under poeticness criteria, we
heuristically joined the two models and generated valid poems in terms of
automatic and human assessments. Final results demonstrate the eligibility and
creativity of our novel heuristically aided approach among Literature
professionals and non-professionals in generating novel Persian poems.
