# Grammatical Error Generation Based on Translated Fragments

arXiv: https://arxiv.org/abs/2104.09933 · HTML: https://mlsanity.com/paper/2104.09933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Marian: Cost-effective High-Quality Neural Machine Translation in C++](https://mlsanity.com/paper/1805.12096/) (2018)
- [Artificial Error Generation with Machine Translation and Syntactic Patterns](https://mlsanity.com/paper/1707.05236/) (2017)
- [Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection](https://mlsanity.com/paper/1810.00668/) (2018)
- [The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction](https://mlsanity.com/paper/1907.08889/) (2019)

## Abstract (arXiv)

We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction. Our method aims at simulating mistakes made by second language learners, and produces a wider range of non-native style language in comparison to state-of-the-art synthetic data creation methods. In addition to purely grammatical errors, our approach generates other types of errors, such as lexical errors. We perform grammatical error correction experiments using neural sequence-to-sequence models, and carry out quantitative and qualitative evaluation. A model trained on data created using our proposed method is shown to outperform a baseline model on test data with a high proportion of errors.