mlsanityData to 7 Oct 2026

A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning

arXiv 1907.01256 ↗First posted 2 Jul 2019cs.CL cs.LGcited by 26

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Abstract (arXiv)

Grammatical error correction can be viewed as a low-resource sequence-to-sequence task, because publicly available parallel corpora are limited. To tackle this challenge, we first generate erroneous versions of large unannotated corpora using a realistic noising function. The resulting parallel corpora are subsequently used to pre-train Transformer models. Then, by sequentially applying transfer learning, we adapt these models to the domain and style of the test set. Combined with a context-aware neural spellchecker, our system achieves competitive results in both restricted and low resource tracks in ACL 2019 BEA Shared Task. We release all of our code and materials for reproducibility.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2019.

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Citing papers per year: 2021: 12; 2022: 2; 2023: 6; 2024: 5; 2025: 1.

26 papers.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  2. 2021Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Modelscited by 56
  3. 2022Grammatical Error Correction: A Survey of the State of the Artcited by 43
  4. 2023Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methodscited by 37
  5. 2021Neural Inverse Text Normalizationcited by 19
  6. 2024GPT-3.5 for Grammatical Error Correctioncited by 18
  7. 2022Czech Grammar Error Correction with a Large and Diverse Corpuscited by 17
  8. 2024Backdoor Attacks on Dense Retrieval via Public and Unintentional Triggerscited by 13
  9. 2021Comparison of Grammatical Error Correction Using Back-Translation Modelscited by 9
  10. 2021Spelling Correction with Denoising Transformercited by 9
  11. 2021Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labelingcited by 8
  12. 2021Understanding Model Robustness to User-generated Noisy Textscited by 8
14 more
  1. 2021System Combination for Grammatical Error Correction Based on Integer Programmingcited by 7
  2. 2024Improving Grammatical Error Correction via Contextual Data Augmentationcited by 6
  3. 2023Transfer learning for conflict and duplicate detection in software requirement pairscited by 5
  4. 2023Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedulecited by 5
  5. 2021A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correctioncited by 4
  6. 2021Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learningcited by 3
  7. 2021Diversity-Driven Combination for Grammatical Error Correctioncited by 3
  8. 2021PicTalky: Augmentative and Alternative Communication Software for Language Developmental Disabilitiescited by 2
  9. 2023Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscriptscited by 2
  10. 2023MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correctioncited by 2
  11. 2023System Combination via Quality Estimation for Grammatical Error Correctioncited by 2
  12. 2024Detection-Correction Structure via General Language Model for Grammatical Error Correctioncited by 1
  13. 2024Multi-head Sequence Tagging Model for Grammatical Error Correctioncited by 0
  14. 2025Time Is Effort: Estimating Human Post-Editing Time for Grammar Error Correction Tool Evaluationcited by 0