mlsanityData to 7 Oct 2026

Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking

arXiv 2207.09217 ↗First posted 17 Jul 2022cs.CLcited by 14

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Abstract (arXiv)

Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. Recently, related researches focus on introducing character similarity from confusion set to enhance the CSC models, ignoring the context of characters that contain richer information. To make better use of contextual information, we propose a simple yet effective Curriculum Learning (CL) framework for the CSC task. With the help of our model-agnostic CL framework, existing CSC models will be trained from easy to difficult as humans learn Chinese characters and achieve further performance improvements. Extensive experiments and detailed analyses on widely used SIGHAN datasets show that our method outperforms previous state-of-the-art methods. More instructively, our study empirically suggests that contextual similarity is more valuable than character similarity for the CSC task.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 17 Jul 2022.

Builds on

8 of 29 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Revisiting Pre-Trained Models for Chinese Natural Language Processingcited by 262
  3. 2021Curriculum Learning: A Surveycited by 257
  4. 2020Spelling Error Correction with Soft-Masked BERTcited by 90
  5. 2017Curriculum Learning and Minibatch Bucketing in Neural Machine Translationcited by 63
  6. 2020SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Checkcited by 57
  7. 2022The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checkingcited by 56
  8. 2021Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checkingcited by 52

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 3; 2025: 3; 2026: 1.

14 papers.

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  1. 2023On the (In)Effectiveness of Large Language Models for Chinese Text Correctioncited by 53
  2. 2023Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characterscited by 21
  3. 2023A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Checkcited by 15
  4. 2024Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correctioncited by 9
  5. 2026Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understandingcited by 6
  6. 2023CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correctioncited by 5
  7. 2024Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Frameworkcited by 4
  8. 2025From Token to Line: Enhancing Code Generation with a Long-Term Perspectivecited by 4
  9. 2022A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Rankingcited by 2
  10. 2023MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correctioncited by 2
  11. 2025Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learningcited by 2
  12. 2025Corrections Meet Explanations: A Unified Framework for Explainable Grammatical Error Correctioncited by 1
2 more
  1. 2022A Chinese Spelling Check Framework Based on Reverse Contrastive Learningcited by 0
  2. 2024EdaCSC: Two Easy Data Augmentation Methods for Chinese Spelling Correctioncited by 0