Challenge: Chinese Grammatical Error Correction (CGEC) aims to generate correct sentences from erroneous sequences.
Approach: They propose a zero-shot approach for spelling error correction that is simple but effective . they propose auxiliary task to predict POS sequence of target sentence .
Outcome: The proposed framework achieves 42.11 F-0.5 on the English GEC dataset outperforms the previous state-of-the-art by a wide margin of 1.30 points.

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Challenge: grammatical error correction (GEC) is a complex task that requires high-quality data from native speakers.
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An Error-Guided Correction Model for Chinese Spelling Error Correction (2022.findings-emnlp)

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Challenge: Existing neural network approaches have achieved great progress on Chinese spelling correction, but there is still room for improvement.
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NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023.findings-acl)

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Challenge: Recent studies on Chinese grammatical error correction focus on learning essays.
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VisCGEC: Benchmarking the Visual Chinese Grammatical Error Correction (2025.naacl-long)

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Challenge: Existing studies on Chinese grammatical error correction ignore multi-modality and faked errors, which pushes techniques far away from real-world scenarios.
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Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)

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Challenge: Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life.
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MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)

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Challenge: Using a multi-reference multi-source evaluation dataset, Chinese grammatical error correction (CGEC) is relatively scarce.
Approach: They propose a multi-reference multi-source evaluation dataset for Chinese grammar error correction . the dataset contains 7,063 sentences written by Chinese-as-a-Second-Language learners .
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Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models (2021.acl-short)

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Challenge: Experimental results show that a sequence-to-sequence learning framework with neural networks can be effective for Chinese Spelling Correction (CSC)
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Heterogeneous Recycle Generation for Chinese Grammatical Error Correction (2020.coling-main)

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Challenge: Recent work in the field of grammatical error correction (GEC) rely on neural machine translation-based models.
Approach: They propose a heterogeneous approach to Chinese grammatical error correction using NMT-based models, sequence editing models, and a spell checker.
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Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology (2025.coling-main)

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Challenge: In educational settings, GEC systems provide immediate and consistent feedback to both native (L1) and non-native (L2) language learners.
Approach: They propose a framework that provides detailed feedback on 12-16% of all errors by identifying them under a new error typology, specific enough to uncover subtle differences in error patterns between L1 and L2 writings.
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UnifiedGEC: Integrating Grammatical Error Correction Approaches for Multi-languages with a Unified Framework (2025.coling-demos)

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Challenge: Existing tools for GEC have been developed to support research on grammatical errors, but there is no comprehensive evaluation on these models.
Approach: They propose an open-source framework for Grammatical Error Correction that integrates 5 widely-used GEC models and compares their performance on 7 datasets in different languages.
Outcome: The proposed framework compares 5 widely-used models on 7 datasets in different languages.

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