# The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction

arXiv: https://arxiv.org/abs/1907.08889 · HTML: https://mlsanity.com/paper/1907.08889/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2019.

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

- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [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)
- [Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule](https://mlsanity.com/paper/2311.11813/) (2023)
- [GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding](https://mlsanity.com/paper/2311.08191/) (2023)
- [To Err Is Human, but Llamas Can Learn It Too](https://mlsanity.com/paper/2403.05493/) (2024)
- [Grammatical Error Generation Based on Translated Fragments](https://mlsanity.com/paper/2104.09933/) (2021)
- [ChemFixer: Correcting Invalid Molecules to Unlock Previously Unseen Chemical Space](https://mlsanity.com/paper/2511.13758/) (2025)

## Abstract (arXiv)

In recent years, sequence-to-sequence models have been very effective for end-to-end grammatical error correction (GEC). As creating human-annotated parallel corpus for GEC is expensive and time-consuming, there has been work on artificial corpus generation with the aim of creating sentences that contain realistic grammatical errors from grammatically correct sentences. In this paper, we investigate the impact of using recent neural models for generating errors to help neural models to correct errors. We conduct a battery of experiments on the effect of data size, models, and comparison with a rule-based approach.