A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification

Arabic poetry follows intricate rhythmic patterns called ‘arūḍ’ (prosody), so its automated categorization is difficult. Although earlier studies mostly depend on conventional machine learning and recurrent neural networks, we evaluate the efficiency of transformer-based models, which have not been extensively investigated for this job. In this work, for Arabic meter classification we investigate pretrained transformer models such as Arabic Bidirectional Encoder Representations from Transformers (Arabic-BERT), BERT base Arabic (AraBERT), Arabic Efficiently Learning an Encoder that Classifies Token Replacements Accurately (AraELECTRA), Computational Approaches to Modeling Arabic BERT (CAMeLBERT), Multi-dialect Arabic BERT (MARBERT), and modern Arabic BERT (ARBERT), and deep learning models like Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Gated Recurrent Units (BiGRU). The half-verse data with 14 meters were employed in this study. The CAMeLBERT and BiLSTM model shows 91% accuracy compared to other models. We investigate feature significance and model behavior using a public dataset utilizing the Local Interpretable Model-agnostic Explanations (LIME) interpretability approach. These results show the benefits and constraints of every method, therefore opening the path for further developments in Arabic poetry analysis with deep learning.