# A Nested Attention Neural Hybrid Model for Grammatical Error Correction

arXiv: https://arxiv.org/abs/1707.02026 · HTML: https://mlsanity.com/paper/1707.02026/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 12 of 12)

- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods](https://mlsanity.com/paper/2305.18156/) (2023)
- [LM-Critic: Language Models for Unsupervised Grammatical Error Correction](https://mlsanity.com/paper/2109.06822/) (2021)
- [Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond](https://mlsanity.com/paper/2205.11484/) (2022)
- [GPT-3.5 for Grammatical Error Correction](https://mlsanity.com/paper/2405.08469/) (2024)
- [Rethinking Perturbations in Encoder-Decoders for Fast Training](https://mlsanity.com/paper/2104.01853/) (2021)
- [Comparison of Grammatical Error Correction Using Back-Translation Models](https://mlsanity.com/paper/2104.07848/) (2021)
- [Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora](https://mlsanity.com/paper/2305.17906/) (2023)
- [An Error-Guided Correction Model for Chinese Spelling Error Correction](https://mlsanity.com/paper/2301.06323/) (2023)
- [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)
- [Chinese grammatical error correction based on knowledge distillation](https://mlsanity.com/paper/2208.00351/) (2022)

## Abstract (arXiv)

Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography.