# Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction

arXiv: https://arxiv.org/abs/2501.00334 · HTML: https://mlsanity.com/paper/2501.00334/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 31 Dec 2024.

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

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Competence-based Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/1903.09848/) (2019)
- [CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation](https://mlsanity.com/paper/2109.05729/) (2021)
- [Norm-Based Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/2006.02014/) (2020)
- [The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking](https://mlsanity.com/paper/2203.00991/) (2022)
- [On the (In)Effectiveness of Large Language Models for Chinese Text Correction](https://mlsanity.com/paper/2307.09007/) (2023)
- [EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce](https://mlsanity.com/paper/2308.06966/) (2023)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2210.12484/) (2022)
- [Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking](https://mlsanity.com/paper/2210.10320/) (2022)
- [Data Weighted Training Strategies for Grammatical Error Correction](https://mlsanity.com/paper/2008.02976/) (2020)
- [Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.10442/) (2022)
- [Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters](https://mlsanity.com/paper/2311.11268/) (2023)
- [When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models](https://mlsanity.com/paper/2402.11100/) (2024)
- [Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation](https://mlsanity.com/paper/1906.01130/) (2019)
- [Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation](https://mlsanity.com/paper/2205.10884/) (2022)
- [Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction](https://mlsanity.com/paper/2306.17447/) (2023)
- [Active Relation Discovery: Towards General and Label-aware Open Relation Extraction](https://mlsanity.com/paper/2211.04215/) (2022)
- [From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2211.01625/) (2022)
- [Exploiting Curriculum Learning in Unsupervised Neural Machine Translation](https://mlsanity.com/paper/2109.11177/) (2021)
- [CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction](https://mlsanity.com/paper/2305.10819/) (2023)
- [Grammatical Error Correction via Mixed-Grained Weighted Training](https://mlsanity.com/paper/2311.13848/) (2023)

## Built on by (most cited first, 3 of 3)

- [Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning](https://mlsanity.com/paper/2509.13790/) (2025)
- [CL2GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction](https://mlsanity.com/paper/2509.13672/) (2025)
- [CoCoGEC: Counterfactual Generation for Robust Grammatical Error Correction](https://mlsanity.com/paper/2606.15069/) (2026)

## Abstract (arXiv)

Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences. Recently, Pre-trained Language Models (PLMS) have been employed to improve the performance. However, current approaches ignore that correction difficulty varies across different instances and treat these samples equally, enhancing the challenge of model learning. To address this problem, we propose a multi-granularity Curriculum Learning (CL) framework. Specifically, we first calculate the correction difficulty of these samples and feed them into the model from easy to hard batch by batch. Then Instance-Level CL is employed to help the model optimize in the appropriate direction automatically by regulating the loss function. Extensive experimental results and comprehensive analyses of various datasets prove the effectiveness of our method.