# System Combination for Grammatical Error Correction Based on Integer Programming

arXiv: https://arxiv.org/abs/2111.01465 · HTML: https://mlsanity.com/paper/2111.01465/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](https://mlsanity.com/paper/1804.05940/) (2018)
- [An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction](https://mlsanity.com/paper/1909.00502/) (2019)
- [Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data](https://mlsanity.com/paper/1903.00138/) (2019)
- [A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning](https://mlsanity.com/paper/1907.01256/) (2019)
- [Learning to combine Grammatical Error Corrections](https://mlsanity.com/paper/1906.03897/) (2019)

## Built on by (most cited first, 7 of 7)

- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [Efficient and Interpretable Grammatical Error Correction with Mixture of Experts](https://mlsanity.com/paper/2410.23507/) (2024)
- [Optimizing Class-Level Probability Reweighting Coefficients for Equitable Prompting Accuracy](https://mlsanity.com/paper/2405.07623/) (2024)
- [Ensemble Debiasing Across Class and Sample Levels for Fairer Prompting Accuracy](https://mlsanity.com/paper/2503.05157/) (2025)
- [Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction](https://mlsanity.com/paper/2605.13624/) (2026)
- [Constrained C-Test Generation via Mixed-Integer Programming](https://mlsanity.com/paper/2404.08821/) (2024)
- [ArbESC+: Arabic Enhanced Edit Selection System Combination for Grammatical Error Correction Resolving conflict and improving system combination in Arabic GEC](https://mlsanity.com/paper/2511.14230/) (2025)

## Abstract (arXiv)

In this paper, we propose a system combination method for grammatical error correction (GEC), based on nonlinear integer programming (IP). Our method optimizes a novel F score objective based on error types, and combines multiple end-to-end GEC systems. The proposed IP approach optimizes the selection of a single best system for each grammatical error type present in the data. Experiments of the IP approach on combining state-of-the-art standalone GEC systems show that the combined system outperforms all standalone systems. It improves F0.5 score by 3.61% when combining the two best participating systems in the BEA 2019 shared task, and achieves F0.5 score of 73.08%. We also perform experiments to compare our IP approach with another state-of-the-art system combination method for GEC, demonstrating IP's competitive combination capability.