# Controllable Data Synthesis Method for Grammatical Error Correction

arXiv: https://arxiv.org/abs/1909.13302 · HTML: https://mlsanity.com/paper/1909.13302/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Comparison of Grammatical Error Correction Using Back-Translation Models](https://mlsanity.com/paper/2104.07848/) (2021)
- [YACLC: A Chinese Learner Corpus with Multidimensional Annotation](https://mlsanity.com/paper/2112.15043/) (2021)
- [Improved Text Classification via Test-Time Augmentation](https://mlsanity.com/paper/2206.13607/) (2022)
- [Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction](https://mlsanity.com/paper/2309.11439/) (2023)
- [Toward autocorrection of chemical process flowsheets using large language models](https://mlsanity.com/paper/2312.02873/) (2023)
- [Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning](https://mlsanity.com/paper/2101.12409/) (2021)
- [UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts](https://mlsanity.com/paper/2304.04539/) (2023)

## Abstract (arXiv)

Due to the lack of parallel data in current Grammatical Error Correction (GEC) task, models based on Sequence to Sequence framework cannot be adequately trained to obtain higher performance. We propose two data synthesis methods which can control the error rate and the ratio of error types on synthetic data. The first approach is to corrupt each word in the monolingual corpus with a fixed probability, including replacement, insertion and deletion. Another approach is to train error generation models and further filtering the decoding results of the models. The experiments on different synthetic data show that the error rate is 40% and the ratio of error types is the same can improve the model performance better. Finally, we synthesize about 100 million data and achieve comparable performance as the state of the art, which uses twice as much data as we use.