  This paper proposes a sequence-to-sequence learning approach for Arabic
pronoun resolution, which explores the effectiveness of using advanced natural
language processing (NLP) techniques, specifically Bi-LSTM and the BERT
pre-trained Language Model, in solving the pronoun resolution problem in
Arabic. The proposed approach is evaluated on the AnATAr dataset, and its
performance is compared to several baseline models, including traditional
machine learning models and handcrafted feature-based models. Our results
demonstrate that the proposed model outperforms the baseline models, which
include KNN, logistic regression, and SVM, across all metrics. In addition, we
explore the effectiveness of various modifications to the model, including
concatenating the anaphor text beside the paragraph text as input, adding a
mask to focus on candidate scores, and filtering candidates based on gender and
number agreement with the anaphor. Our results show that these modifications
significantly improve the model's performance, achieving up to 81% on MRR and
71% for F1 score while also demonstrating higher precision, recall, and
accuracy. These findings suggest that the proposed model is an effective
approach to Arabic pronoun resolution and highlights the potential benefits of
leveraging advanced NLP neural models.
