# Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction Systems

arXiv: https://arxiv.org/abs/2309.06520 · HTML: https://mlsanity.com/paper/2309.06520/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics](https://mlsanity.com/paper/2111.09388/) (2021)
- [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019)
- [Parallel Iterative Edit Models for Local Sequence Transduction](https://mlsanity.com/paper/1910.02893/) (2019)
- [ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark](https://mlsanity.com/paper/2303.13648/) (2023)
- [Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction](https://mlsanity.com/paper/2005.00987/) (2020)
- [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://mlsanity.com/paper/2009.11136/) (2020)
- [Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation](https://mlsanity.com/paper/2105.08504/) (2021)
- [An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction](https://mlsanity.com/paper/1909.00502/) (2019)
- [The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1704.01664/) (2017)
- [Ensemble Distillation for Neural Machine Translation](https://mlsanity.com/paper/1702.01802/) (2017)
- [Data Weighted Training Strategies for Grammatical Error Correction](https://mlsanity.com/paper/2008.02976/) (2020)
- [Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction](https://mlsanity.com/paper/2010.03260/) (2020)
- [Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction](https://mlsanity.com/paper/2203.13064/) (2022)
- [Ensemble Distillation Approaches for Grammatical Error Correction](https://mlsanity.com/paper/2012.07535/) (2020)
- [CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models](https://mlsanity.com/paper/2306.05317/) (2023)
- [RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation](https://mlsanity.com/paper/2203.00201/) (2022)

## Built on by (most cited first, 4 of 4)

- [Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM](https://mlsanity.com/paper/2401.02994/) (2024)
- [gec-metrics: A Unified Library for Grammatical Error Correction Evaluation](https://mlsanity.com/paper/2505.19388/) (2025)
- [Case-Based Decision-Theoretic Decoding with Quality Memories](https://mlsanity.com/paper/2509.12677/) (2025)
- [Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction](https://mlsanity.com/paper/2605.13624/) (2026)

## Abstract (arXiv)

For sequence-to-sequence tasks it is challenging to combine individual system outputs. Further, there is also often a mismatch between the decoding criterion and the one used for assessment. Minimum Bayes' Risk (MBR) decoding can be used to combine system outputs in a manner that encourages better alignment with the final assessment criterion. This paper examines MBR decoding for Grammatical Error Correction (GEC) systems, where performance is usually evaluated in terms of edits and an associated F-score. Hence, we propose a novel MBR loss function directly linked to this form of criterion. Furthermore, an approach to expand the possible set of candidate sentences is described. This builds on a current max-voting combination scheme, as well as individual edit-level selection. Experiments on three popular GEC datasets and with state-of-the-art GEC systems demonstrate the efficacy of the proposed MBR approach. Additionally, the paper highlights how varying reward metrics within the MBR decoding framework can provide control over precision, recall, and the F-score in combined GEC systems.