Prosody recognition in Persian poetry

Abstract

Classical Persian poetry, like traditional poetry from other cultures, follows set metrical patterns, known as prosody. Recognizing prosody of a given poetry is very useful in understanding and analyzing Persian language and literature. With the advances in artificial intelligence (AI) techniques, they became popular to recognize prosody. However, the application of advanced AI methodologies to the task of detecting prosody in Persian poetry is not well-explored. Additionally, The lack of an extensive collection of traditional Persian poems, each meticulously annotated with its prosodic pattern, is another challenge. In this paper, first we create a large dataset of prosodic meters including about 1.3 million couplets, which contains detailed prosodic annotations. Then, we introduce five models that harness advanced deep learning methodologies to discern the prosody of Persian poetry. These models include: (i) a transformer-based classifier, (ii) a grapheme-to-phoneme mapping-based method, (iii) a sequence-to-sequence model, (iv) a sequence-to-sequence model with phonemic sequences, and (v) a hybrid approach that leverages the strengths of both the textual information of poetry and its phonemic sequence. Our experimental results reveal that the hybrid model typically outperforms the other models, especially when applied to large samples of the created dataset. Our code is publicly available in https://github.com/m-shahrestani/Prosody-Recognition-in-Persian-Poetry/.