# An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

arXiv: https://arxiv.org/abs/1909.00502 · HTML: https://mlsanity.com/paper/1909.00502/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 30 of 55)

- [Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models](https://mlsanity.com/paper/2105.13318/) (2021)
- [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)
- [SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2210.12484/) (2022)
- [Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond](https://mlsanity.com/paper/2205.11484/) (2022)
- [Error Correction in ASR using Sequence-to-Sequence Models](https://mlsanity.com/paper/2202.01157/) (2022)
- [Generating Bug-Fixes Using Pretrained Transformers](https://mlsanity.com/paper/2104.07896/) (2021)
- [GPT-3.5 for Grammatical Error Correction](https://mlsanity.com/paper/2405.08469/) (2024)
- [DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons](https://mlsanity.com/paper/2105.09352/) (2021)
- [A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model](https://mlsanity.com/paper/2201.10707/) (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)
- [Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction](https://mlsanity.com/paper/2203.13064/) (2022)
- [Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction](https://mlsanity.com/paper/2105.04443/) (2021)
- [Comparison of Grammatical Error Correction Using Back-Translation Models](https://mlsanity.com/paper/2104.07848/) (2021)
- [ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization](https://mlsanity.com/paper/2201.05313/) (2022)
- [BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representation](https://mlsanity.com/paper/2204.07837/) (2022)
- [Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models](https://mlsanity.com/paper/2404.14914/) (2024)
- [System Combination for Grammatical Error Correction Based on Integer Programming](https://mlsanity.com/paper/2111.01465/) (2021)
- [Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction](https://mlsanity.com/paper/2303.14342/) (2023)
- [Grammatical Error Correction as GAN-like Sequence Labeling](https://mlsanity.com/paper/2105.14209/) (2021)
- [Do Grammatical Error Correction Models Realize Grammatical Generalization?](https://mlsanity.com/paper/2106.03031/) (2021)
- [Improving Grammatical Error Correction via Contextual Data Augmentation](https://mlsanity.com/paper/2406.17456/) (2024)
- [Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule](https://mlsanity.com/paper/2311.11813/) (2023)
- [Adapting LLMs for Minimal-edit Grammatical Error Correction](https://mlsanity.com/paper/2506.13148/) (2025)
- [A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction](https://mlsanity.com/paper/2111.03294/) (2021)
- [CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2211.08158/) (2022)
- [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)

## Abstract (arXiv)

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set (F0.5=65.0) and the official test set of the BEA-2019 shared task (F0.5=70.2) without making any modifications to the model architecture.