# Type-Driven Multi-Turn Corrections for Grammatical Error Correction

arXiv: https://arxiv.org/abs/2203.09136 · HTML: https://mlsanity.com/paper/2203.09136/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019)
- [JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction](https://mlsanity.com/paper/1702.04066/) (2017)
- [Parallel Iterative Edit Models for Local Sequence Transduction](https://mlsanity.com/paper/1910.02893/) (2019)
- [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://mlsanity.com/paper/2009.11136/) (2020)
- [Corpora Generation for Grammatical Error Correction](https://mlsanity.com/paper/1904.05780/) (2019)
- [Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding](https://mlsanity.com/paper/2106.04970/) (2021)
- [Improving BERT with Syntax-aware Local Attention](https://mlsanity.com/paper/2012.15150/) (2020)
- [Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction](https://mlsanity.com/paper/2010.03260/) (2020)
- [Structural Information Preserving for Graph-to-Text Generation](https://mlsanity.com/paper/2102.06749/) (2021)
- [Asynchronous Bidirectional Decoding for Neural Machine Translation](https://mlsanity.com/paper/1801.05122/) (2018)
- [Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment](https://mlsanity.com/paper/2001.08728/) (2020)
- [Grammatical Error Correction as GAN-like Sequence Labeling](https://mlsanity.com/paper/2105.14209/) (2021)
- [Open Challenge for Correcting Errors of Speech Recognition Systems](https://mlsanity.com/paper/2001.03041/) (2020)

## Built on by (most cited first, 9 of 9)

- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [Revisiting Grammatical Error Correction Evaluation and Beyond](https://mlsanity.com/paper/2211.01635/) (2022)
- [An Extended Sequence Tagging Vocabulary for Grammatical Error Correction](https://mlsanity.com/paper/2302.05913/) (2023)
- [Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule](https://mlsanity.com/paper/2311.11813/) (2023)
- [Detection-Correction Structure via General Language Model for Grammatical Error Correction](https://mlsanity.com/paper/2405.17804/) (2024)
- [Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2502.08507/) (2025)
- [Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction](https://mlsanity.com/paper/2307.02127/) (2023)
- [RE2: Improving Chinese Grammatical Error Correction via Retrieving Appropriate Examples with Explanation](https://mlsanity.com/paper/2509.26038/) (2025)
- [Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2606.15416/) (2026)

## Abstract (arXiv)

Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performing multiple iterations of corrections during inference. Previous studies mainly focus on the data augmentation approach to combat the exposure bias, which suffers from two drawbacks. First, they simply mix additionally-constructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections. Second, they ignore the interdependence between different types of corrections. In this paper, we propose a Type-Driven Multi-Turn Corrections approach for GEC. Using this approach, from each training instance, we additionally construct multiple training instances, each of which involves the correction of a specific type of errors. Then, we use these additionally-constructed training instances and the original one to train the model in turn. Experimental results and in-depth analysis show that our approach significantly benefits the model training. Particularly, our enhanced model achieves state-of-the-art single-model performance on English GEC benchmarks. We release our code at Github.