# Artificial Error Generation with Machine Translation and Syntactic Patterns

arXiv: https://arxiv.org/abs/1707.05236 · HTML: https://mlsanity.com/paper/1707.05236/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 11 of 11)

- [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)
- [Comparison of Grammatical Error Correction Using Back-Translation Models](https://mlsanity.com/paper/2104.07848/) (2021)
- [Spelling Correction with Denoising Transformer](https://mlsanity.com/paper/2105.05977/) (2021)
- [Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling](https://mlsanity.com/paper/2105.11872/) (2021)
- [Understanding Model Robustness to User-generated Noisy Texts](https://mlsanity.com/paper/2110.07428/) (2021)
- [To Err Is Human, but Llamas Can Learn It Too](https://mlsanity.com/paper/2403.05493/) (2024)
- [Grammatical Error Correction for Code-Switched Sentences by Learners of English](https://mlsanity.com/paper/2404.12489/) (2024)
- [Organic Data-Driven Approach for Turkish Grammatical Error Correction and LLMs](https://mlsanity.com/paper/2405.15320/) (2024)
- [Grammatical Error Generation Based on Translated Fragments](https://mlsanity.com/paper/2104.09933/) (2021)
- [Error-Supervised Synthetic Learner Writing for Automated Essay Scoring](https://mlsanity.com/paper/2609.23573/) (2026)

## Abstract (arXiv)

Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. We propose treating error generation as a machine translation task, where grammatically correct text is translated to contain errors. In addition, we explore a system for extracting textual patterns from an annotated corpus, which can then be used to insert errors into grammatically correct sentences. Our experiments show that the inclusion of artificially generated errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.