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

arXiv: https://arxiv.org/abs/2504.21475 · HTML: https://mlsanity.com/paper/2504.21475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://mlsanity.com/paper/2012.15516/) (2020)
- [Training Sparse Mixture Of Experts Text Embedding Models](https://mlsanity.com/paper/2502.07972/) (2025)
- [Multi-channel Reverse Dictionary Model](https://mlsanity.com/paper/1912.08441/) (2019)
- [BERT for Monolingual and Cross-Lingual Reverse Dictionary](https://mlsanity.com/paper/2009.14790/) (2020)
- [Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture](https://mlsanity.com/paper/1606.00025/) (2016)
- [Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning](https://mlsanity.com/paper/2407.21139/) (2024)
- [A Unified Model for Reverse Dictionary and Definition Modelling](https://mlsanity.com/paper/2205.04602/) (2022)
- [Using Multi-Sense Vector Embeddings for Reverse Dictionaries](https://mlsanity.com/paper/1904.01451/) (2019)

## Built on by (most cited first, 1 of 1)

- [MURAD: A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset](https://mlsanity.com/paper/2601.21512/) (2026)

## Abstract (arXiv)

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.