# Context-aware Stand-alone Neural Spelling Correction

arXiv: https://arxiv.org/abs/2011.06642 · HTML: https://mlsanity.com/paper/2011.06642/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2108.02859/) (2021)
- [Context-aware Adversarial Attack on Named Entity Recognition](https://mlsanity.com/paper/2309.08999/) (2023)
- [Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input](https://mlsanity.com/paper/2310.14110/) (2023)
- [Investigating Neurons and Heads in Transformer-based LLMs for Typographical Errors](https://mlsanity.com/paper/2502.19669/) (2025)

## Abstract (arXiv)

Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the stand-alone spelling correction problem, which only corrects the spelling of each token without additional token insertion or deletion, by utilizing both spelling information and global context representations. We present a simple yet powerful solution that jointly detects and corrects misspellings as a sequence labeling task by fine-turning a pre-trained language model. Our solution outperforms the previous state-of-the-art result by 12.8% absolute F0.5 score.