# Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction

arXiv: https://arxiv.org/abs/2010.03260 · HTML: https://mlsanity.com/paper/2010.03260/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 24 of 24)

- [Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation](https://mlsanity.com/paper/2203.16487/) (2022)
- [Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding](https://mlsanity.com/paper/2106.04970/) (2021)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [EdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start](https://mlsanity.com/paper/2205.12209/) (2022)
- [EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation](https://mlsanity.com/paper/2202.07959/) (2022)
- [Text Generation with Text-Editing Models](https://mlsanity.com/paper/2206.07043/) (2022)
- [Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks](https://mlsanity.com/paper/2212.01350/) (2022)
- [Lossless Acceleration for Seq2seq Generation with Aggressive Decoding](https://mlsanity.com/paper/2205.10350/) (2022)
- [Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation](https://mlsanity.com/paper/2205.10884/) (2022)
- [Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting](https://mlsanity.com/paper/2101.00416/) (2021)
- [Type-Driven Multi-Turn Corrections for Grammatical Error Correction](https://mlsanity.com/paper/2203.09136/) (2022)
- [Non-autoregressive Text Editing with Copy-aware Latent Alignments](https://mlsanity.com/paper/2310.07821/) (2023)
- [Improving Seq2Seq Grammatical Error Correction via Decoding Interventions](https://mlsanity.com/paper/2310.14534/) (2023)
- [From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2211.01625/) (2022)
- [Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction](https://mlsanity.com/paper/2309.11439/) (2023)
- [Alirector: Alignment-Enhanced Chinese Grammatical Error Corrector](https://mlsanity.com/paper/2402.04601/) (2024)
- [Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction Systems](https://mlsanity.com/paper/2309.06520/) (2023)
- [Diversity-Driven Combination for Grammatical Error Correction](https://mlsanity.com/paper/2110.15149/) (2021)
- [GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding](https://mlsanity.com/paper/2311.08191/) (2023)
- [RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans](https://mlsanity.com/paper/2309.10898/) (2023)
- [Detection-Correction Structure via General Language Model for Grammatical Error Correction](https://mlsanity.com/paper/2405.17804/) (2024)
- [SiniticMTError: A Machine Translation Dataset with Error Annotations for Sinitic Languages](https://mlsanity.com/paper/2509.20557/) (2025)
- [UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models](https://mlsanity.com/paper/2104.04770/) (2021)
- [Improving Audio Caption Fluency with Automatic Error Correction](https://mlsanity.com/paper/2306.10090/) (2023)

## Abstract (arXiv)

We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.