# Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation

arXiv: https://arxiv.org/abs/1804.05945 · HTML: https://mlsanity.com/paper/1804.05945/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [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)
- [Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction](https://mlsanity.com/paper/2303.14342/) (2023)
- [Do Grammatical Error Correction Models Realize Grammatical Generalization?](https://mlsanity.com/paper/2106.03031/) (2021)
- [Towards Lithuanian grammatical error correction](https://mlsanity.com/paper/2203.09963/) (2022)
- [Proficiency Matters Quality Estimation in Grammatical Error Correction](https://mlsanity.com/paper/2201.06199/) (2022)

## Abstract (arXiv)

We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT). The hybrid system achieves new state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC system preserves the accuracy of SMT output and, at the same time, generates more fluent sentences as it typical for NMT. Our analysis shows that the created systems are closer to reaching human-level performance than any other GEC system reported so far.