# SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check

arXiv: https://arxiv.org/abs/2004.14166 · HTML: https://mlsanity.com/paper/2004.14166/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2020.

## Built on by (most cited first, 30 of 57)

- [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)
- [Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking](https://mlsanity.com/paper/2210.10320/) (2022)
- [General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining](https://mlsanity.com/paper/2203.10929/) (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)
- [Rethinking Masked Language Modeling for Chinese Spelling Correction](https://mlsanity.com/paper/2305.17721/) (2023)
- [Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models](https://mlsanity.com/paper/2105.14813/) (2021)
- [Chinese Spelling Correction as Rephrasing Language Model](https://mlsanity.com/paper/2308.08796/) (2023)
- [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check](https://mlsanity.com/paper/2310.09119/) (2023)
- [Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking](https://mlsanity.com/paper/2207.09217/) (2022)
- [Disentangled Phonetic Representation for Chinese Spelling Correction](https://mlsanity.com/paper/2305.14783/) (2023)
- [Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction](https://mlsanity.com/paper/2306.17447/) (2023)
- [C-LLM: Learn to Check Chinese Spelling Errors Character by Character](https://mlsanity.com/paper/2406.16536/) (2024)
- [Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction](https://mlsanity.com/paper/2211.13252/) (2022)
- [uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers](https://mlsanity.com/paper/2209.07068/) (2022)
- [MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction](https://mlsanity.com/paper/2210.11720/) (2022)
- [From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2211.01625/) (2022)
- [A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models](https://mlsanity.com/paper/2410.04027/) (2024)
- [An Error-Guided Correction Model for Chinese Spelling Error Correction](https://mlsanity.com/paper/2301.06323/) (2023)
- [SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking](https://mlsanity.com/paper/2210.17168/) (2022)
- [Error-Robust Retrieval for Chinese Spelling Check](https://mlsanity.com/paper/2211.07843/) (2022)
- [Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework](https://mlsanity.com/paper/2402.11422/) (2024)
- [Compressed-Language Models for Understanding Compressed File Formats: a JPEG Exploration](https://mlsanity.com/paper/2405.17146/) (2024)
- [BSpell: A CNN-Blended BERT Based Bangla Spell Checker](https://mlsanity.com/paper/2208.09709/) (2022)
- [A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check](https://mlsanity.com/paper/2307.13655/) (2023)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check](https://mlsanity.com/paper/2308.08176/) (2023)
- [Enhance Robustness of Language Models Against Variation Attack through Graph Integration](https://mlsanity.com/paper/2404.12014/) (2024)
- [Chinese Spelling Correction: A Comprehensive Survey of Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2502.11508/) (2025)

## Abstract (arXiv)

Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledge between Chinese characters. However, they take the similarity knowledge as either an external input resource or just heuristic rules. This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN). The model builds a graph over the characters, and SpellGCN is learned to map this graph into a set of inter-dependent character classifiers. These classifiers are applied to the representations extracted by another network, such as BERT, enabling the whole network to be end-to-end trainable. Experiments (The dataset and all code for this paper are available at https://github.com/ACL2020SpellGCN/SpellGCN) are conducted on three human-annotated datasets. Our method achieves superior performance against previous models by a large margin.