Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry

Abstract

Persian poetry has consistently expressed its philosophy, wisdom, speech, and rationale based on its couplets, making it an enigmatic language on its own to both native and non-native speakers. Nevertheless, the noticeable gap between Persian prose and poems has left the two pieces of literature mediumless. Having curated a parallel corpus of prose and their equivalent poems, we introduce a novel Neural Machine Translation approach for translating prose to ancient Persian poetry using transformer-based language models in an exceptionally low-resource setting. Translating input prose into ancient Persian poetry presents two primary challenges: In addition to being reasonable in conveying the same context as the input prose, the translation must also satisfy poetic standards. Hence, we designed our method consisting of three stages. First, we trained a transformer model from scratch to obtain an initial translations of the input prose. Next, we designed a set of heuristics to leverage contextually rich initial translations and produced a poetic masked template. In the last stage, we pretrained different variations of BERT on a poetry corpus to use the masked language modelling technique to obtain final translations. During the evaluation process, we considered both automatic and human assessment. The final results demonstrate the eligibility and creativity of our novel heuristically aided approach among Literature professionals and non-professionals in generating novel Persian poems.