# MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction

arXiv: https://arxiv.org/abs/2204.10994 · HTML: https://mlsanity.com/paper/2204.10994/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 61 references that are in the map.
- First posted on arXiv 23 Apr 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding](https://mlsanity.com/paper/1908.04577/) (2019)
- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019)
- [JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction](https://mlsanity.com/paper/1702.04066/) (2017)
- [Parallel Iterative Edit Models for Local Sequence Transduction](https://mlsanity.com/paper/1910.02893/) (2019)
- [CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation](https://mlsanity.com/paper/2109.05729/) (2021)
- [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](https://mlsanity.com/paper/1804.05940/) (2018)
- [Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model](https://mlsanity.com/paper/2005.11849/) (2020)
- [UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language](https://mlsanity.com/paper/2103.16997/) (2021)
- [Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2106.01609/) (2021)
- [Czech Grammar Error Correction with a Large and Diverse Corpus](https://mlsanity.com/paper/2201.05590/) (2022)
- [Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses](https://mlsanity.com/paper/2010.07574/) (2020)
- [Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification](https://mlsanity.com/paper/1804.11254/) (2018)
- [YACLC: A Chinese Learner Corpus with Multidimensional Annotation](https://mlsanity.com/paper/2112.15043/) (2021)
- [RethinkCWS: Is Chinese Word Segmentation a Solved Task?](https://mlsanity.com/paper/2011.06858/) (2020)
- [Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?](https://mlsanity.com/paper/1904.02927/) (2019)

## Built on by (most cited first, 30 of 33)

- [LawBench: Benchmarking Legal Knowledge of Large Language Models](https://mlsanity.com/paper/2309.16289/) (2023)
- [On the (In)Effectiveness of Large Language Models for Chinese Text Correction](https://mlsanity.com/paper/2307.09007/) (2023)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2210.12484/) (2022)
- [Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.10442/) (2022)
- [GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning](https://mlsanity.com/paper/2307.13923/) (2023)
- [FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.12364/) (2022)
- [Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction](https://mlsanity.com/paper/2306.17447/) (2023)
- [Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications](https://mlsanity.com/paper/2405.11579/) (2024)
- [How Ready Are Generative Pre-trained Large Language Models for Explaining Bengali Grammatical Errors?](https://mlsanity.com/paper/2406.00039/) (2024)
- [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://mlsanity.com/paper/2305.16023/) (2023)
- [Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction](https://mlsanity.com/paper/2402.11420/) (2024)
- [Non-autoregressive Text Editing with Copy-aware Latent Alignments](https://mlsanity.com/paper/2310.07821/) (2023)
- [From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2211.01625/) (2022)
- [RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation](https://mlsanity.com/paper/2310.07299/) (2023)
- [CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction](https://mlsanity.com/paper/2305.10819/) (2023)
- [Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task](https://mlsanity.com/paper/2307.03972/) (2023)
- [FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotation](https://mlsanity.com/paper/2311.04906/) (2023)
- [CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2211.08158/) (2022)
- [Focus Is What You Need For Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.12692/) (2022)
- [CSED: A Chinese Semantic Error Diagnosis Corpus](https://mlsanity.com/paper/2305.05183/) (2023)
- [Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2501.00334/) (2024)
- [ChineseErrorCorrector3-4B: State-of-the-Art Chinese Spelling and Grammar Corrector](https://mlsanity.com/paper/2511.17562/) (2025)
- [Mining Error Templates for Grammatical Error Correction](https://mlsanity.com/paper/2206.11569/) (2022)
- [Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction?](https://mlsanity.com/paper/2305.15183/) (2023)
- [PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction](https://mlsanity.com/paper/2412.03230/) (2024)
- [Visual Subtitle Feature Enhanced Video Outline Generation](https://mlsanity.com/paper/2208.11307/) (2022)
- [An Analysis of Language Frequency and Error Correction for Esperanto](https://mlsanity.com/paper/2402.09696/) (2024)
- [Leveraging Prompt-Tuning for Bengali Grammatical Error Explanation Using Large Language Models](https://mlsanity.com/paper/2504.05642/) (2025)
- [COLA-GEC: A Bidirectional Framework for Enhancing Grammatical Acceptability and Error Correction](https://mlsanity.com/paper/2507.11867/) (2025)

## Abstract (arXiv)

This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7,063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources. Each sentence is corrected by three annotators, and their corrections are carefully reviewed by a senior annotator, resulting in 2.3 references per sentence. We conduct experiments with two mainstream CGEC models, i.e., the sequence-to-sequence model and the sequence-to-edit model, both enhanced with large pretrained language models, achieving competitive benchmark performance on previous and our datasets. We also discuss CGEC evaluation methodologies, including the effect of multiple references and using a char-based metric. Our annotation guidelines, data, and code are available at urlhttps://github.com/HillZhang1999/MuCGEC.