# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 2 Literature Review
[38] outlined a three-part methodology: applying similarity metrics to evaluate sentence quality, generating refined Arabic samples using AraGPT-2, and assessing sentiment classification using AraBERT. Their research also addressed class imbalance issues prevalent in Arabic sentiment datasets. [PERSON] et al. [10] explored Arabic Natural Language Inference (NJI) using transformer models such as AraBERT and RoBERTa. The study introduced a novel pretraining strategy combining Named Entity Recognition, allowing for better identification of conflicts and entailments. Using a custom dataset derived from publicly available resources and linguistically enriched pretraining, AraBERT achieved an accuracy of 88.1%, outperforming RoBERTa enhanced with language-specific features. In another study, [PERSON] et al. [39] developed a linguistic model for Arabic poetry analysis based on a BERT architecture pretrained from scratch using an extensive Arabic poetry corpus. This model incorporated 768 hidden units, 10 encoder layers, and 12 attention heads per layer, along with a vocabulary of 50,000 words, enabling it to capture the full a breadth of poetic forms and idiomatic expressions in the Arabic literature. A hybrid methodology integrating deep learning with text representation techniques has been proposed to enhance the classification of Arabic news articles [9]. After comprehensive preprocessing steps including text cleaning, tokenization, lemmatization, and data augmentation, a proprietary attention embedding layer was employed to capture contextual relationships in the text. With data augmentation, the model surpassed the state-of-the-art Arabic language model AraBERTv2, achieving a classification accuracy of 97.69% [40]. In a study by [PERSON] et al. [41], the authors introduced a novel AI text classifier designed to address the specific challenges in detecting AI-generated Arabic texts. This approach optimized two transformer models: the Cross-lingual Language Model and AraELECTRA [42]. Achieving an accuracy of 81%, the proposed models outperformed existing AI detectors. CAMeLBERT, when trained on an Arabic poetry dataset, achieved a performance accuracy of 80.9% [43]. [PERSON] et al. [44] enhanced Arabic text representation by utilizing FastText word embeddings, followed by an attention-based BiGRU classification model. This approach included a complete preprocessing pipeline and incorporated hyperparameter tuning to efficiently explore the search space and improve model performance. Similarly, [PERSON] et al. [45] applied NLP techniques to detect hate speech in the Arabic literature across various dialects, leveraging BERT-based pretrained models to capture the linguistic complexity of the Arabic language. [PERSON] and [PERSON] [46] proposed a dotless Arabic text representation to reduce vocabulary size while maintaining comparable NLP performance. Their approach demonstrated up to a 50% reduction in vocabulary, with successful results across tasks such as language modeling and translation. Recent efforts in Arabic text-to-speech synthesis have also focused on enhancing emotional expressiveness, particularly in applications designed to assist visually impaired people [47]. Transformer-based architectures have also been adapted for domain-specific applications. For instance, [PERSON] et al. [48] developed a graph transformer model for detecting fraudulent financial transactions. While their work targets the financial sector, it highlights the increasing trend of tailoring transformer models for intricate and domain-specific frameworks. [PERSON] et al. [49] explored the use of explainable AI methods--such as LIME and SHapley Additive exPlanations (SHAP)--to clarify model decision-making in Arabic linguistic applications. The integration of explainability tools is considered essential for broader adoption and trust in transformer-based models within domain-specific contexts.