# Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning

arXiv: https://arxiv.org/abs/2108.01141 · HTML: https://mlsanity.com/paper/2108.01141/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning](https://mlsanity.com/paper/1907.01256/) (2019)
- [Arabic Text Diacritization Using Deep Neural Networks](https://mlsanity.com/paper/1905.01965/) (2019)
- [Spelling Correction with Denoising Transformer](https://mlsanity.com/paper/2105.05977/) (2021)
- [Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models](https://mlsanity.com/paper/1809.01534/) (2018)
- [Building a Lemmatizer and a Spell-checker for Sorani Kurdish](https://mlsanity.com/paper/1809.10763/) (2018)
- [Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data](https://mlsanity.com/paper/1811.00238/) (2018)

## Built on by (most cited first, 3 of 3)

- [AraSpell: A Deep Learning Approach for Arabic Spelling Correction](https://mlsanity.com/paper/2405.06981/) (2024)
- [Large Language Models and Arabic Content: A Review](https://mlsanity.com/paper/2505.08004/) (2025)
- [Automating Conflict-Aware ACL Configurations with Natural Language Intents](https://mlsanity.com/paper/2508.17990/) (2025)

## Abstract (arXiv)

Soft spelling errors are a class of spelling mistakes that is widespread among native Arabic speakers and foreign learners alike. Some of these errors are typographical in nature. They occur due to orthographic variations of some Arabic letters and the complex rules that dictate their correct usage. Many people forgo these rules, and given the identical phonetic sounds, they often confuse such letters. In this paper, we propose a bidirectional long short-term memory network that corrects this class of errors. We develop, train, evaluate, and compare a set of BiLSTM networks. We approach the spelling correction problem at the character level. We handle Arabic texts from both classical and modern standard Arabic. We treat the problem as a one-to-one sequence transcription problem. Since the soft Arabic errors class encompasses omission and addition mistakes, to preserve the one-to-one sequence transcription, we propose a simple low-resource yet effective technique that maintains the one-to-one sequencing and avoids using a costly encoder-decoder architecture. We train the BiLSTM models to correct the spelling mistakes using transformed input and stochastic error injection approaches. We recommend a configuration that has two BiLSTM layers, uses the dropout regularization, and is trained using the latter training approach with error injection rate of 40%. The best model corrects 96.4% of the injected errors and achieves a low character error rate of 1.28% on a real test set of soft spelling mistakes.