# Neural Network Translation Models for Grammatical Error Correction

arXiv: https://arxiv.org/abs/1606.00189 · HTML: https://mlsanity.com/paper/1606.00189/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [An Error-Guided Correction Model for Chinese Spelling Error Correction](https://mlsanity.com/paper/2301.06323/) (2023)
- [Efficient and Interpretable Grammatical Error Correction with Mixture of Experts](https://mlsanity.com/paper/2410.23507/) (2024)
- [System Combination via Quality Estimation for Grammatical Error Correction](https://mlsanity.com/paper/2310.14947/) (2023)
- [Mining Error Templates for Grammatical Error Correction](https://mlsanity.com/paper/2206.11569/) (2022)

## Abstract (arXiv)

Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. However, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. In this paper, we address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model. These neural networks can generalize better by using continuous space representation of words and learn non-linear mappings. Moreover, they can leverage contextual information from the source sentence more effectively. By adding these two components, we achieve statistically significant improvement in accuracy for grammatical error correction over a state-of-the-art GEC system.