---
title: "Towards Arabic Sentence Simplification via — Khallaf 2022"
description: "The authors build what they describe as the first sentence-level simplification system for Modern Standard Arabic."
authors: ["Nouran Khallaf", "Serge Sharoff", "Rasha Soliman"]
year: 2022
venue: "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)"
source: "https://aclanthology.org/2022.wanlp-1.5/"
doi: "10.18653/v1/2022.wanlp-1.5"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/khallaf-2022-arabic-sentence-simplification-classification-generative/"
---
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# Towards Arabic Sentence Simplification via Classification and Generative Approaches

**Authors:** [Nouran Khallaf](https://md.research.elamur.ai/free/en/authors/nouran-khallaf/), [Serge Sharoff](https://md.research.elamur.ai/free/en/authors/serge-sharoff/), Rasha Soliman · **Year:** 2022 · **Venue:** Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.wanlp-1.5/) · [PDF](https://aclanthology.org/2022.wanlp-1.5.pdf) · [DOI 10.18653/v1/2022.wanlp-1.5](https://doi.org/10.18653/v1/2022.wanlp-1.5) · [OpenAlex](https://openalex.org/W4385573015)

**Citations:** 7 citations (openalex-snapshot · 2026-10-14) · [3](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The authors build what they describe as the first sentence-level simplification system for Modern Standard Arabic. Their training resource is a parallel corpus they assembled from the novel "Saaq al-Bambuu" and its authorized simplified edition for learners of Arabic as a second language. Sentences were paired, and words were aligned with Eflomal. Two strategies are compared. The first treats simplification as lexical classification: it identifies complex words with a CEFR vocabulary list and MADAMIRA features, then proposes and ranks substitutes using Arabic-BERT masked prediction and fastText embeddings, with post-editing rules on top. The second treats simplification as monolingual translation, fine-tuning mT5 as a Seq2Seq model. Against BERTScore, the combined lexical pipeline reaches about 0.97 on P, R and F-1, while mT5 gets roughly 0.70 F-1. Manual analysis exposes many mT5 failures, though it sometimes produces paraphrases better than the human reference. The work matters because it supplies a parallel resource and a baseline for a language with very few simplification tools.

## Key points
- The corpus contains 2980 aligned complex/simple sentence pairs labelled with CEFR levels. The simple side (A+B) has 34447 tokens and the complex side (C) has 46521.
- Alignment-derived operation counts:
  - REWRITE (word copied unchanged): 21899
  - DELETE: 12561
  - REPLACE: 9082
  - ADD: 362
- The lexical pipeline follows four steps: CWI, then SG, then SR, then SS. Its tools are the fastText `ar.300.bin` vectors, Arabic-BERT (trained on about 8.2B words), and MADAMIRA for POS and morphology.
- BERTScore results:

  | Model | P | R | F-1 |
  |---|---|---|---|
  | mT5 | 0.72 | 0.68 | 0.70 |
  | Arabic-BERT + fastText with rules | 0.97 | 0.97 | 0.97 |

- In manual checking of 299 mT5 test outputs:
  - only 31 were properly simplified;
  - about 120 were incomplete;
  - 64 were ill-formed or meaningless;
  - repeated phrases were a recurring defect.

## Interesting ideas
- Automatically labelling each word in an aligned original/simplified pair as kept, deleted, replaced or added turns a parallel corpus into a measurable profile of the editing operations it contains. — For Mova (back): Aligning input text with the output of the text → graph → text compressor and tallying these four operations would show precisely where regeneration drops, swaps or invents material.
- Complex words are sorted from hardest to easiest CEFR level, and only one is masked and simplified per pass, giving one candidate sentence per target word instead of rewriting everything at once. — For Mova (back): Generation could apply simplifying or paraphrasing edits one controlled slot at a time, so each change stays individually explainable and reversible.
- A contextual masked model proposes candidates, and a static embedding re-ranks them by cosine similarity to the original word. Hand rules then remove candidates sharing the original lemma or disagreeing in POS and number, and fall back to embeddings when the model outputs `[UNK]`. — For Mova (world): Synonym or hypernym substitution drawn from WordNet-style knowledge could be filtered the same way, using a lemma-difference check plus morphological agreement so that substitutes remain grammatical.
- Strong automatic scores (BERTScore around 0.70 for mT5) hid the fact that only about a tenth of outputs were really acceptable, which the manual error categories exposed. — For Mova (learn): Evaluation of generated or repaired output should pair similarity metrics with an error taxonomy (incomplete, ill-formed, repetitive, more complex), so that a high score cannot mask degenerate output.

*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: [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/) · [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

3 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 1 not yet.

- [The SAMER Arabic Text Simplification Corpus](https://md.research.elamur.ai/free/en/papers/alhafni-2024-samer-arabic-text-simplification-corpus/)
- [Leveraging AI to Bridge Classical Arabic and Modern Standard Arabic for Text Simplification](https://md.research.elamur.ai/free/en/papers/altammami-2025-leveraging-ai-bridge-classical-arabic/)

## Cite

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

```bibtex
@inproceedings{khallaf2022arabic,
  title = {{Towards Arabic Sentence Simplification via Classification and Generative Approaches}},
  author = {Khallaf, Nouran and Sharoff, Serge and Soliman, Rasha},
  booktitle = {Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)},
  year = {2022},
  doi = {10.18653/v1/2022.wanlp-1.5},
  url = {https://doi.org/10.18653/v1/2022.wanlp-1.5}
}
```

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## References

43 references, 13 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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