# Neural Language Correction with Character-Based Attention

arXiv: https://arxiv.org/abs/1603.09727 · HTML: https://mlsanity.com/paper/1603.09727/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2016.

## Built on by (most cited first, 18 of 18)

- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [LM-Critic: Language Models for Unsupervised Grammatical Error Correction](https://mlsanity.com/paper/2109.06822/) (2021)
- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
- [Neural OCR Post-Hoc Correction of Historical Corpora](https://mlsanity.com/paper/2102.00583/) (2021)
- [Towards the Unseen: Iterative Text Recognition by Distilling from Errors](https://mlsanity.com/paper/2107.12081/) (2021)
- [From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2211.01625/) (2022)
- [Sketch-based Creativity Support Tools using Deep Learning](https://mlsanity.com/paper/2111.09991/) (2021)
- [Hierarchical Character Tagger for Short Text Spelling Error Correction](https://mlsanity.com/paper/2109.14259/) (2021)
- [Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition](https://mlsanity.com/paper/2111.08400/) (2021)
- [Towards Lithuanian grammatical error correction](https://mlsanity.com/paper/2203.09963/) (2022)
- [Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting](https://mlsanity.com/paper/2310.13321/) (2023)
- [Leveraging What's Overfixed: Post-Correction via LLM Grammatical Error Overcorrection](https://mlsanity.com/paper/2509.20811/) (2025)
- [Neural Grammatical Error Correction for Romanian](https://mlsanity.com/paper/2604.23627/) (2026)
- [Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models](https://mlsanity.com/paper/2203.07911/) (2022)
- [Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction?](https://mlsanity.com/paper/2305.15183/) (2023)
- [Visual Intelligence through Human Interaction](https://mlsanity.com/paper/2111.06913/) (2021)
- [Spelling Correction in Healthcare Query-Answer Systems: Methods, Retrieval Impact, and Empirical Evaluation](https://mlsanity.com/paper/2603.19249/) (2026)
- [Beyond Detection: Ethical Foundations for Automated Dyslexic Error Attribution](https://mlsanity.com/paper/2604.01853/) (2026)

## Abstract (arXiv)

Natural language correction has the potential to help language learners improve their writing skills. While approaches with separate classifiers for different error types have high precision, they do not flexibly handle errors such as redundancy or non-idiomatic phrasing. On the other hand, word and phrase-based machine translation methods are not designed to cope with orthographic errors, and have recently been outpaced by neural models. Motivated by these issues, we present a neural network-based approach to language correction. The core component of our method is an encoder-decoder recurrent neural network with an attention mechanism. By operating at the character level, the network avoids the problem of out-of-vocabulary words. We illustrate the flexibility of our approach on dataset of noisy, user-generated text collected from an English learner forum. When combined with a language model, our method achieves a state-of-the-art F0.5-score on the CoNLL 2014 Shared Task. We further demonstrate that training the network on additional data with synthesized errors can improve performance.