mlsanityData to 7 Oct 2026

A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check

arXiv 2310.09119 ↗First posted 13 Oct 2023cs.CLcited by 15

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Abstract (arXiv)

In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion. In this paper, we propose to decompose the CSC workflow into detection, reasoning, and searching subtasks so that the rich external knowledge about the Chinese language can be leveraged more directly and efficiently. Specifically, we design a plug-and-play detection-and-reasoning module that is compatible with existing SOTA non-autoregressive CSC models to further boost their performance. We find that the detection-and-reasoning module trained for one model can also benefit other models. We also study the primary interpretability provided by the task decomposition. Extensive experiments and detailed analyses demonstrate the effectiveness and competitiveness of the proposed module.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 29 of its 57 references that are in the map. First posted on arXiv 13 Oct 2023.

Builds on

29 of 57 references.

References · 29

  1. 2020Revisiting Pre-Trained Models for Chinese Natural Language Processingcited by 262
  2. 2019ERNIE 2.0: A Continual Pre-training Framework for Language Understandingcited by 230
  3. 2020Spelling Error Correction with Soft-Masked BERTcited by 90
  4. 2021ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Informationcited by 65
  5. 2021Are we ready for a new paradigm shift? A Survey on Visual Deep MLPcited 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. 2023On the (In)Effectiveness of Large Language Models for Chinese Text Correctioncited by 53
  9. 2021Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checkingcited by 52
  10. 2022Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checkingcited by 31
  11. 2022Contrastive Learning with Hard Negative Entities for Entity Set Expansioncited by 26
  12. 2022Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correctioncited by 25
17 more
  1. 2022Automatic Context Pattern Generation for Entity Set Expansioncited by 21
  2. 2022M3ST: Mix at Three Levels for Speech Translationcited by 21
  3. 2021Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correctioncited by 19
  4. 2022Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularitycited by 19
  5. 2021Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Modelscited by 17
  6. 2022Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checkingcited by 14
  7. 2021Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognitioncited by 13
  8. 2023Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correctioncited by 13
  9. 2023SSVMR: Saliency-based Self-training for Video-Music Retrievalcited by 12
  10. 2022Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledgecited by 11
  11. 2022Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotationscited by 9
  12. 2022uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkerscited by 9
  13. 2023Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linkingcited by 7
  14. 2023An Error-Guided Correction Model for Chinese Spelling Error Correctioncited by 5
  15. 2023CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correctioncited by 5
  16. 2022Focus Is What You Need For Chinese Grammatical Error Correctioncited by 3
  17. 2022AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applicationscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 7; 2026: 1.

15 papers.

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  1. 2025LLM Agents for Education: Advances and Applicationscited by 59
  2. 2023Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characterscited by 21
  3. 2025DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokenscited by 21
  4. 2024ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questionscited by 10
  5. 2024Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correctioncited by 9
  6. 2026Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understandingcited by 6
  7. 2025Mixture of Small and Large Models for Chinese Spelling Checkcited by 5
  8. 2024Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Frameworkcited by 4
  9. 2025From Token to Line: Enhancing Code Generation with a Long-Term Perspectivecited by 4
  10. 2025Chinese Spelling Correction: A Comprehensive Survey of Progress, Challenges, and Opportunitiescited by 2
  11. 2024Bi-DCSpell: A Bi-directional Detector-Corrector Interactive Framework for Chinese Spelling Checkcited by 1
  12. 2024EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correctioncited by 1
3 more
  1. 2024DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Checkcited by 1
  2. 2025Corrections Meet Explanations: A Unified Framework for Explainable Grammatical Error Correctioncited by 1
  3. 2025FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writingcited by 0