Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines

TL;DR

This work tackles the gap in Arabic reverse dictionary (RD) tooling by introducing a transformer-based semi-encoder architecture with geometrically decreasing hidden layers to enhance semantic alignment between user descriptions and target words. It couples a formal abstraction of the RD task with a comprehensive dataset quality framework and an open-source library (RDTL) to support reproducible research. Empirically, Arabic-specific pre-trained models, notably ARBERTv2, outperform multilingual embeddings (best rank 0.0644), and an extensive dataset analysis yields eight standards for high-quality Arabic RD resources. The contributions advance Arabic computational linguistics and offer practical aids for language learning, academic writing, and professional Arabic communication, while enabling future work in multi-task learning and morphology-aware RD models.

Abstract

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.

Figures (3)

• Figure 1: Overview of the encoder-decoder flow with embedding and target loss.
• Figure 2: Accuracy distribution
• Figure 3: Summary of RD writing standards.