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ArbESC+: Arabic Enhanced Edit Selection System Combination for Grammatical Error Correction Resolving conflict and improving system combination in Arabic GEC

arXiv 2511.14230 ↗First posted 18 Nov 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

Grammatical Error Correction (GEC) is an important aspect of natural language processing. Arabic has a complicated morphological and syntactic structure, posing a greater challenge than other languages. Even though modern neural models have improved greatly in recent years, the majority of previous attempts used individual models without taking into account the potential benefits of combining different systems. In this paper, we present one of the first multi-system approaches for correcting grammatical errors in Arabic, the Arab Enhanced Edit Selection System Complication (ArbESC+). Several models are used to collect correction proposals, which are represented as numerical features in the framework. A classifier determines and implements the appropriate corrections based on these features. In order to improve output quality, the framework uses support techniques to filter overlapping corrections and estimate decision reliability. A combination of AraT5, ByT5, mT5, AraBART, AraBART+Morph+GEC, and Text editing systems gave better results than a single model alone, with F0.5 at 82.63% on QALB-14 test data, 84.64% on QALB-15 L1 data, and 65.55% on QALB-15 L2 data. As one of the most significant contributions of this work, it's the first Arab attempt to integrate linguistic error correction. Improving existing models provides a practical step towards developing advanced tools that will benefit users and researchers of Arabic text processing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 42 references that are in the map. First posted on arXiv 18 Nov 2025.

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References · 21

  1. 2017Attention Is All You Needcited by 60,361
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  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  6. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,043
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  8. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 362
  9. 2021A Simple Recipe for Multilingual Grammatical Error Correctioncited by 108
  10. 2021AraT5: Text-to-Text Transformers for Arabic Language Generationcited by 63
  11. 2020Seq2Edits: Sequence Transduction Using Span-level Edit Operationscited by 58
  12. 2022Grammatical Error Correction: A Survey of the State of the Artcited by 43
9 more
  1. 2023Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigationcited by 22
  2. 2021Automatic Error Type Annotation for Arabiccited by 21
  3. 2019Learning to combine Grammatical Error Correctionscited by 9
  4. 2025Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Studycited by 9
  5. 2021System Combination for Grammatical Error Correction Based on Integer Programmingcited by 7
  6. 2023Byte-Level Grammatical Error Correction Using Synthetic and Curated Corporacited by 7
  7. 2023ChatGPT for Arabic Grammatical Error Correctioncited by 5
  8. 2024Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correctioncited by 1
  9. 2025Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Modelcited by 1

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