# Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction

arXiv: https://arxiv.org/abs/2306.17447 · HTML: https://mlsanity.com/paper/2306.17447/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 70 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://mlsanity.com/paper/1907.12412/) (2019)
- [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)
- [Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation](https://mlsanity.com/paper/2304.01746/) (2023)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation](https://mlsanity.com/paper/2109.05729/) (2021)
- [SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check](https://mlsanity.com/paper/2004.14166/) (2020)
- [The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking](https://mlsanity.com/paper/2203.00991/) (2022)
- [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://mlsanity.com/paper/2105.12306/) (2021)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking](https://mlsanity.com/paper/2210.10320/) (2022)
- [Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2106.01609/) (2021)
- [Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity](https://mlsanity.com/paper/2210.10996/) (2022)
- [A Comprehensive Survey of Grammar Error Correction](https://mlsanity.com/paper/2005.06600/) (2020)
- [Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models](https://mlsanity.com/paper/2105.14813/) (2021)
- [Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation](https://mlsanity.com/paper/2205.10884/) (2022)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [Open Challenge for Correcting Errors of Speech Recognition Systems](https://mlsanity.com/paper/2001.03041/) (2020)
- [Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives](https://mlsanity.com/paper/1802.09345/) (2018)
- [An Alignment-Agnostic Model for Chinese Text Error Correction](https://mlsanity.com/paper/2104.07190/) (2021)

## Built on by (most cited first, 13 of 13)

- [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check](https://mlsanity.com/paper/2310.09119/) (2023)
- [Refine Knowledge of Large Language Models via Adaptive Contrastive Learning](https://mlsanity.com/paper/2502.07184/) (2025)
- [Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction](https://mlsanity.com/paper/2402.11420/) (2024)
- [Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding](https://mlsanity.com/paper/2603.18472/) (2026)
- [Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework](https://mlsanity.com/paper/2402.11422/) (2024)
- [From Token to Line: Enhancing Code Generation with a Long-Term Perspective](https://mlsanity.com/paper/2504.07433/) (2025)
- [Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansion](https://mlsanity.com/paper/2501.00330/) (2024)
- [Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2501.00334/) (2024)
- [MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction](https://mlsanity.com/paper/2310.11671/) (2023)
- [Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning](https://mlsanity.com/paper/2509.13790/) (2025)
- [EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correction](https://mlsanity.com/paper/2407.00924/) (2024)
- [Corrections Meet Explanations: A Unified Framework for Explainable Grammatical Error Correction](https://mlsanity.com/paper/2502.15261/) (2025)
- [CL2GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction](https://mlsanity.com/paper/2509.13672/) (2025)

## Abstract (arXiv)

Chinese Text Error Correction (CTEC) aims to detect and correct errors in the input text, which benefits human daily life and various downstream tasks. Recent approaches mainly employ Pre-trained Language Models (PLMs) to resolve CTEC. Although PLMs have achieved remarkable success in CTEC, we argue that previous studies still overlook the importance of human thinking patterns. To enhance the development of PLMs for CTEC, inspired by humans' daily error-correcting behavior, we propose a novel model-agnostic progressive learning framework, named ProTEC, which guides PLMs-based CTEC models to learn to correct like humans. During the training process, ProTEC guides the model to learn text error correction by incorporating these sub-tasks into a progressive paradigm. During the inference process, the model completes these sub-tasks in turn to generate the correction results. Extensive experiments and detailed analyses demonstrate the effectiveness and efficiency of our proposed model-agnostic ProTEC framework.