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An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

arXiv 1909.00502 ↗First posted 2 Sep 2019cs.CLcited by 55

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Abstract (arXiv)

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set (F0.5=65.0) and the official test set of the BEA-2019 shared task (F0.5=70.2) without making any modifications to the model architecture.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Sep 2019.

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Citing papers per year: 2021: 15; 2022: 12; 2023: 11; 2024: 11; 2025: 3; 2026: 3.

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  1. 2021Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Modelscited by 56
  2. 2022Grammatical Error Correction: A Survey of the State of the Artcited by 43
  3. 2023Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methodscited by 37
  4. 2021LM-Critic: Language Models for Unsupervised Grammatical Error Correctioncited by 33
  5. 2022SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parsercited by 32
  6. 2022Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyondcited by 20
  7. 2022Error Correction in ASR using Sequence-to-Sequence Modelscited by 19
  8. 2021Generating Bug-Fixes Using Pretrained Transformerscited by 18
  9. 2024GPT-3.5 for Grammatical Error Correctioncited by 18
  10. 2021DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletonscited by 17
  11. 2022A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Modelcited by 16
  12. 2022Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generationcited by 15
38 more
  1. 2021Improving Sequence-to-Sequence Pre-training via Sequence Span Rewritingcited by 13
  2. 2022Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correctioncited by 12
  3. 2021Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correctioncited by 11
  4. 2021Comparison of Grammatical Error Correction Using Back-Translation Modelscited by 9
  5. 2022ExtraPhrase: Efficient Data Augmentation for Abstractive Summarizationcited by 8
  6. 2022BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representationcited by 8
  7. 2024Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Modelscited by 8
  8. 2021System Combination for Grammatical Error Correction Based on Integer Programmingcited by 7
  9. 2023Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correctioncited by 7
  10. 2021Grammatical Error Correction as GAN-like Sequence Labelingcited by 6
  11. 2021Do Grammatical Error Correction Models Realize Grammatical Generalization?cited by 6
  12. 2024Improving Grammatical Error Correction via Contextual Data Augmentationcited by 6
  13. 2023Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedulecited by 5
  14. 2025Adapting LLMs for Minimal-edit Grammatical Error Correctioncited by 5
  15. 2021A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correctioncited by 4
  16. 2022CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parsercited by 4
  17. 2023Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction Systemscited by 4
  18. 2021Diversity-Driven Combination for Grammatical Error Correctioncited by 3
  19. 2022Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?cited by 3
  20. 2024Revisiting Meta-evaluation for Grammatical Error Correctioncited by 3
  21. 2024To Err Is Human, but Llamas Can Learn It Toocited by 3
  22. 2021Hierarchical Character Tagger for Short Text Spelling Error Correctioncited by 2
  23. 2023Bangla Grammatical Error Detection Using T5 Transformer Modelcited by 2
  24. 2023MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correctioncited by 2
  25. 2023Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmentingcited by 2
  26. 2023System Combination via Quality Estimation for Grammatical Error Correctioncited by 2
  27. 2024Grammatical Error Correction for Code-Switched Sentences by Learners of Englishcited by 2
  28. 2026Neural Grammatical Error Correction for Romaniancited by 2
  29. 2021Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learningcited by 1
  30. 2021An Alignment-Agnostic Model for Chinese Text Error Correctioncited by 1
  31. 2022Judge a Sentence by Its Content to Generate Grammatical Errorscited by 1
  32. 2023Bidirectional Transformer Reranker for Grammatical Error Correctioncited by 1
  33. 2024A Combination of BERT and Transformer for Vietnamese Spelling Correctioncited by 1
  34. 2024Organic Data-Driven Approach for Turkish Grammatical Error Correction and LLMscited by 1
  35. 2023IdEALS: Idiomatic Expressions for Advancement of Language Skillscited by 0
  36. 2023Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correctioncited by 0
  37. 2024Assessing the Efficacy of Grammar Error Correction: A Human Evaluation Approach in the Japanese Contextcited by 0
  38. 2024Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detectioncited by 0

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