Advancing Arabic Reverse Dictionary Systems: A Transformer-Based
  Approach with Dataset Construction Guidelines
Authors: name: Serry Sibaee, name: Samar Ahmed, name: Abdullah Al Harbi, name: Omer Nacar, name: Adel Ammar, name: Yasser Habashi, name: Wadii Boulila

This study addresses the critical gap in Arabic natural language processing
by developing an effective Arabic Reverse Dictionary (RD) system that enables
users to find words based on their descriptions or meanings. We present a novel
transformer-based approach with a semi-encoder neural network architecture
featuring geometrically decreasing layers that achieves state-of-the-art
results for Arabic RD tasks. Our methodology incorporates a comprehensive
dataset construction process and establishes formal quality standards for
Arabic lexicographic definitions. Experiments with various pre-trained models
demonstrate that Arabic-specific models significantly outperform general
multilingual embeddings, with ARBERTv2 achieving the best ranking score
(0.0644). Additionally, we provide a formal abstraction of the reverse
dictionary task that enhances theoretical understanding and develop a modular,
extensible Python library (RDTL) with configurable training pipelines. Our
analysis of dataset quality reveals important insights for improving Arabic
definition construction, leading to eight specific standards for building
high-quality reverse dictionary resources. This work contributes significantly
to Arabic computational linguistics and provides valuable tools for language
learning, academic writing, and professional communication in Arabic.