# Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data

arXiv: https://arxiv.org/abs/1811.00238 · HTML: https://mlsanity.com/paper/1811.00238/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

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

- [Misspelling Correction with Pre-trained Contextual Language Model](https://mlsanity.com/paper/2101.03204/) (2021)
- [Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning](https://mlsanity.com/paper/2108.01141/) (2021)
- [TiSpell: A Semi-Masked Methodology for Tibetan Spelling Correction covering Multi-Level Error with Data Augmentation](https://mlsanity.com/paper/2505.08037/) (2025)

## Abstract (arXiv)

We propose a nested recurrent neural network (nested RNN) model for English spelling error correction and generate pseudo data based on phonetic similarity to train it. The model fuses orthographic information and context as a whole and is trained in an end-to-end fashion. This avoids feature engineering and does not rely on a noisy channel model as in traditional methods. Experiments show that the proposed method is superior to existing systems in correcting spelling errors.