# Spelling Correction as a Foreign Language

arXiv: https://arxiv.org/abs/1705.07371 · HTML: https://mlsanity.com/paper/1705.07371/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2017.

## Built on by (most cited first, 7 of 7)

- [Chinese Spelling Correction as Rephrasing Language Model](https://mlsanity.com/paper/2308.08796/) (2023)
- [GIO: Gradient Information Optimization for Training Dataset Selection](https://mlsanity.com/paper/2306.11670/) (2023)
- [Spelling Correction with Denoising Transformer](https://mlsanity.com/paper/2105.05977/) (2021)
- [VSEC: Transformer-based Model for Vietnamese Spelling Correction](https://mlsanity.com/paper/2111.00640/) (2021)
- [A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages](https://mlsanity.com/paper/2308.09435/) (2023)
- [Multi-teacher Distillation for Multilingual Spelling Correction](https://mlsanity.com/paper/2311.11518/) (2023)
- [A Comprehensive Approach to Misspelling Correction with BERT and Levenshtein Distance](https://mlsanity.com/paper/2407.17383/) (2024)

## Abstract (arXiv)

In this paper, we reformulated the spell correction problem as a machine translation task under the encoder-decoder framework. This reformulation enabled us to use a single model for solving the problem that is traditionally formulated as learning a language model and an error model. This model employs multi-layer recurrent neural networks as an encoder and a decoder. We demonstrate the effectiveness of this model using an internal dataset, where the training data is automatically obtained from user logs. The model offers competitive performance as compared to the state of the art methods but does not require any feature engineering nor hand tuning between models.