Meter classification of Arabic poems using deep bidirectional recurrent neural networks

Maged Saeed AlShaibani, Zaid Alyafeai, Irfan Ahmad

Abstract: Highlights • Automatic classification of poem meters using deep bidirectional recurrent neural networks. • Avoids the need to diacritize the texts of Arabic poems which is error prone. • Evaluated on a large database of Arabic poems created by the authors for this specific task in order to serve as a benchmark. • Classification rate of 94.32% on an independent test set for 14 poem meters. Abstract Poetry is an important component of any language. Much of a nation’s history and culture are documented in poems. A poem has a rhythmic flow which is quite different as compared to a prose. Each language has its own set of rhythmical structures for poems, called meters. Identifying the meters of Arabic poems is a lengthy and complicated process. To classify a poem’s meter, the text of the poem should be encoded in a special Arudi form which needs complex rule-based transformations before another set of rules can be used to finally classify the meters. This paper introduces a novel method for classifying poem meters of Arabic poems using RNN-based deep learning. It bypasses the need to transform the poem to the Arudi form as well as the need to explicitly encode the complex rules that are usually followed to determine the meter. The presented method was evaluated on a large dataset collected specifically for this purpose. We are able to classify the poem meters with an accuracy of 94.32% on an independent test set.