---
title: "Advancing Arabic Reverse Dictionary Systems — Sibaee 2025"
description: "A reverse dictionary (RD) takes a description of a meaning and returns the word that fits it."
authors: ["Serry Sibaee", "Samar Ahmed", "Abdullah Al Harbi", "Omer Nacar", "Adel Ammar", "Yasser Habashi", "Wadii Boulila"]
year: 2025
venue: "arXiv:2504.21475"
source: "https://arxiv.org/abs/2504.21475"
doi: "10.48550/arxiv.2504.21475"
arxiv: "2504.21475"
licence: "CC BY-NC-ND 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sibaee-2025-advancing-arabic-reverse-dictionary-systems/"
---
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# Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines

**Authors:** [Serry Sibaee](https://md.research.elamur.ai/free/en/authors/serry-sibaee/), Samar Ahmed, Abdullah Al Harbi, [Omer Nacar](https://md.research.elamur.ai/free/en/authors/omer-nacar/), [Adel Ammar](https://md.research.elamur.ai/free/en/authors/adel-ammar/), Yasser Habashi, [Wadii Boulila](https://md.research.elamur.ai/free/en/authors/wadii-boulila/) · **Year:** 2025 · **Venue:** arXiv:2504.21475 · **Licence:** [CC BY-NC-ND 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2504.21475) · [PDF](https://arxiv.org/pdf/2504.21475) · [DOI 10.48550/arxiv.2504.21475](https://doi.org/10.48550/arxiv.2504.21475) · [arXiv 2504.21475](https://arxiv.org/abs/2504.21475) · [OpenAlex](https://openalex.org/W4417523325)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [1](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
A reverse dictionary (RD) takes a description of a meaning and returns the word that fits it. This helps with tip-of-the-tongue moments, with choosing exact terms in academic or legal writing, and with vocabulary learning. Arabic has lagged behind English, French or Chinese here because of its rich morphology, diglossia and frequently omitted diacritics. The KSAA-RD shared tasks of 2023 and 2024 were organised to address this gap. The authors treat RD as embedding-based word retrieval and give the task a formal description. Their model places a "semi-encoder" network on top of pre-trained transformer encoders, with hidden layers whose sizes shrink geometrically. Across several pre-trained models, Arabic-specific encoders clearly beat general multilingual embeddings, and ARBERTv2 reaches the best ranking score (0.0644). The authors also checked random samples of the dataset by hand and found recurring flaws in the definitions. They turned these findings into eight standards for writing RD data. All code is released as RDTL, an open-source Python library with configurable training pipelines. The work shows that definition quality deserves as much care as model choice.

## Key points
- Architecture: a pre-trained transformer encoder followed by a semi-encoder head whose hidden layers decrease geometrically in size. The authors report state-of-the-art results on Arabic RD.
- Encoder comparison: Arabic-specific pre-trained models outperform multilingual embeddings, and ARBERTv2 gives the best ranking score of 0.0644.
- Recurring dataset flaws found in the sample review:
  - Only a narrow technical sense is given. For example, "customary" gets just its legal meaning, "body" is defined as the main part of a garment, and "chord" gets a geometry sense instead of the bowstring.
  - Idioms are used where an explanation is needed.
  - Headwords are phrased with needless length.
  - Lists of synonyms stand in for real definitions.
- The eight standards are:
  1. Meaning over morphology.
  2. Unambiguous pronoun references.
  3. General sense before specialised senses.
  4. Domain stated explicitly.
  5. No figurative wording.
  6. No redundancy.
  7. Descriptions rather than bare synonyms.
  8. Uniform, logical structure.
- Released resources: the RDTL library (GitHub: serrysibaee/reverse_dictionary) and a formal abstraction of the RD task intended to support reproducibility.

## Interesting ideas
- The authors draw random samples of the training data, group the defects they see into named categories, and turn each category into a written rule. This makes data cleaning a repeatable checklist rather than an ad-hoc fix. — For Mova (learn): Audit random samples of silver trees and frames the same way, and convert each recurring error type into an explicit check for LLM teacher tiers or into a self-induced repair rule.
- A definition should give the broadest sense first, and any specialised sense should carry an explicit domain label. This prevents a technical reading from silently replacing the everyday one. — For Mova (world): When compiling WordNet/Wikidata senses into the ontology, order senses general-first and tag domain-specific senses, so the text world picks the everyday reading unless the context signals a domain.
- The paper treats a definition made only of synonyms as uninformative, since it never explains the concept. — For Mova (back): When generating an explanation of a word or entity from the meaning graph, prefer a class-plus-distinguishing-features description over a bare synonym, and score the generator on this.
- The RD task itself is a clean probe of semantic representation: given a description, the system must recover the exact word, and the result can be ranked. — For Mova (answer): Add "which word means …" items to the school-course tests, checking whether the frames built from the description lead to the right lexical entry, with distractor words as negative controls.
- The classifier head narrows step by step, with hidden layers shrinking geometrically, which is a simple and principled way to size an MLP on top of a frozen encoder. — For Mova (tree): Try a geometric funnel as the scoring head in gparse, giving a small, easy-to-tune layer schedule above the transformer.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cited by

Cited by

1 paper of our library cites it (of the 140884 whose bibliographies we read); each has a page here.

- [MURAD: A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset](https://md.research.elamur.ai/free/en/papers/sibaee-2026-murad-large-scale-multi-domain/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{sibaee2025advancing,
  title = {{Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines}},
  author = {Sibaee, Serry and Ahmed, Samar and Harbi, Abdullah Al and Nacar, Omer and Ammar, Adel and Habashi, Yasser and Boulila, Wadii},
  year = {2025},
  doi = {10.48550/arxiv.2504.21475},
  eprint = {2504.21475},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.21475}
}
```

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## References

36 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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