# Automatic Error Type Annotation for Arabic

arXiv: https://arxiv.org/abs/2109.08068 · HTML: https://mlsanity.com/paper/2109.08068/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Classifying Syntactic Errors in Learner Language](https://mlsanity.com/paper/2010.11032/) (2020)
- [SERRANT: a syntactic classifier for English Grammatical Error Types](https://mlsanity.com/paper/2104.02310/) (2021)

## Built on by (most cited first, 21 of 21)

- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
- [Jawaher: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking](https://mlsanity.com/paper/2503.00231/) (2025)
- [Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation](https://mlsanity.com/paper/2210.14389/) (2022)
- [Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2312.08400/) (2023)
- [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://mlsanity.com/paper/2503.00985/) (2025)
- [Exploiting Dialect Identification in Automatic Dialectal Text Normalization](https://mlsanity.com/paper/2407.03020/) (2024)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [ChatGPT for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2308.04492/) (2023)
- [Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection](https://mlsanity.com/paper/2503.17739/) (2025)
- [Gazelle: An Instruction Dataset for Arabic Writing Assistance](https://mlsanity.com/paper/2410.18163/) (2024)
- [Multilingual Grammatical Error Annotation: Combining Language-Agnostic Framework with Language-Specific Flexibility](https://mlsanity.com/paper/2506.07719/) (2025)
- [Computational Morphology and Lexicography Modeling of Modern Standard Arabic Nominals](https://mlsanity.com/paper/2402.00385/) (2024)
- [Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2411.04588/) (2024)
- [Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model](https://mlsanity.com/paper/2502.05312/) (2025)
- [ARWI: Arabic Write and Improve](https://mlsanity.com/paper/2504.11814/) (2025)
- [Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection](https://mlsanity.com/paper/2407.11854/) (2024)
- [Lemmatization as a Classification Task: Results from Arabic across Multiple Genres](https://mlsanity.com/paper/2506.18399/) (2025)
- [ArbESC+: Arabic Enhanced Edit Selection System Combination for Grammatical Error Correction Resolving conflict and improving system combination in Arabic GEC](https://mlsanity.com/paper/2511.14230/) (2025)
- [ArabiGEE: A Hierarchical Taxonomy for Arabic Grammatical Error Explanation](https://mlsanity.com/paper/2606.10765/) (2026)
- [STAGEET: Stage-wise Typed Edit Tagging for Grammatical Error Correction with Arabic as a Case Study](https://mlsanity.com/paper/2608.28614/) (2026)

## Abstract (arXiv)

We present ARETA, an automatic error type annotation system for Modern Standard Arabic. We design ARETA to address Arabic's morphological richness and orthographic ambiguity. We base our error taxonomy on the Arabic Learner Corpus (ALC) Error Tagset with some modifications. ARETA achieves a performance of 85.8% (micro average F1 score) on a manually annotated blind test portion of ALC. We also demonstrate ARETA's usability by applying it to a number of submissions from the QALB 2014 shared task for Arabic grammatical error correction. The resulting analyses give helpful insights on the strengths and weaknesses of different submissions, which is more useful than the opaque M2 scoring metrics used in the shared task. ARETA employs a large Arabic morphological analyzer, but is completely unsupervised otherwise. We make ARETA publicly available.