mlsanityData to 14 Oct 2026

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction

arXiv 2210.10442 ↗First posted 19 Oct 2022cs.CLcited by 25

Not on the map. Why?

Abstract (arXiv)

Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life. Recently, many data-driven approaches are proposed for the development of CGEC research. However, there are two major limitations in the CGEC field: First, the lack of high-quality annotated training corpora prevents the performance of existing CGEC models from being significantly improved. Second, the grammatical errors in widely used test sets are not made by native Chinese speakers, resulting in a significant gap between the CGEC models and the real application. In this paper, we propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors. Additionally, we present a challenging CGEC benchmark derived entirely from errors made by native Chinese speakers in real-world scenarios. Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 16 of its 46 references that are in the map. First posted on arXiv 19 Oct 2022.

Builds on

16 of 46 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  5. 2017Decoupled Weight Decay Regularizationcited by 1,831
  6. 2020Revisiting Pre-Trained Models for Chinese Natural Language Processingcited by 266
  7. 2020GECToR -- Grammatical Error Correction: Tag, Not Rewritecited by 177
  8. 2019StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understandingcited by 109
  9. 2019Encode, Tag, Realize: High-Precision Text Editingcited by 99
  10. 2022The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checkingcited by 56
  11. 2022MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correctioncited by 33
  12. 2021Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correctioncited by 19
4 more
  1. 2020A Comprehensive Survey of Grammar Error Correctioncited by 17
  2. 2022Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generationcited by 15
  3. 2021YACLC: A Chinese Learner Corpus with Multidimensional Annotationcited by 8
  4. 2020Open Challenge for Correcting Errors of Speech Recognition Systemscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 12; 2024: 5; 2025: 6; 2026: 1.

25 papers.

Sort:

By area:

  1. 2023On the (In)Effectiveness of Large Language Models for Chinese Text Correctioncited by 54
  2. 2023LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzlescited by 41
  3. 2023GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuningcited by 23
  4. 2023Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characterscited by 22
  5. 2024When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Modelscited by 22
  6. 2023A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Checkcited by 15
  7. 2023MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entitiescited by 13
  8. 2023NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Textscited by 9
  9. 2024Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correctioncited by 9
  10. 2026Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understandingcited by 7
  11. 2023CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correctioncited by 5
  12. 2023Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Taskcited by 5
13 more
  1. 2024Alirector: Alignment-Enhanced Chinese Grammatical Error Correctorcited by 5
  2. 2025From Token to Line: Enhancing Code Generation with a Long-Term Perspectivecited by 4
  3. 2022Focus Is What You Need For Chinese Grammatical Error Correctioncited by 3
  4. 2024Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansioncited by 3
  5. 2024Loss-Aware Curriculum Learning for Chinese Grammatical Error Correctioncited by 3
  6. 2025MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuningcited by 3
  7. 2023Retrieval-Augmented Meta Learning for Low-Resource Text Classificationcited by 2
  8. 2025Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learningcited by 2
  9. 2025ChineseErrorCorrector3-4B: State-of-the-Art Chinese Spelling and Grammar Correctorcited by 2
  10. 2023Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detectioncited by 1
  11. 2023An Anchor Learning Approach for Citation Field Learningcited by 0
  12. 2025CL2GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correctioncited by 0
  13. 2025RE2: Improving Chinese Grammatical Error Correction via Retrieving Appropriate Examples with Explanationcited by 0