# Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity

arXiv: https://arxiv.org/abs/2210.10996 · HTML: https://mlsanity.com/paper/2210.10996/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://mlsanity.com/paper/2106.16038/) (2021)
- [SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check](https://mlsanity.com/paper/2004.14166/) (2020)
- [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://mlsanity.com/paper/2105.12306/) (2021)

## Built on by (most cited first, 19 of 19)

- [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check](https://mlsanity.com/paper/2310.09119/) (2023)
- [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)
- [Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking](https://mlsanity.com/paper/2403.08492/) (2024)
- [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)
- [Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning](https://mlsanity.com/paper/2411.17679/) (2024)
- [RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check](https://mlsanity.com/paper/2308.08176/) (2023)
- [Learning from Mistakes: Self-correct Adversarial Training for Chinese Unnatural Text Correction](https://mlsanity.com/paper/2412.17279/) (2024)
- [Chinese Spelling Correction: A Comprehensive Survey of Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2502.11508/) (2025)
- [Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction](https://mlsanity.com/paper/2407.15498/) (2024)
- [PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction](https://mlsanity.com/paper/2412.03230/) (2024)
- [Unveiling the Impact of Multimodal Features on Chinese Spelling Correction: From Analysis to Design](https://mlsanity.com/paper/2504.07661/) (2025)
- [Breaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon](https://mlsanity.com/paper/2505.22184/) (2025)
- [A Chinese Spelling Check Framework Based on Reverse Contrastive Learning](https://mlsanity.com/paper/2210.13823/) (2022)
- [A Coin Has Two Sides: A Novel Detector-Corrector Framework for Chinese Spelling Correction](https://mlsanity.com/paper/2409.04150/) (2024)
- [EdaCSC: Two Easy Data Augmentation Methods for Chinese Spelling Correction](https://mlsanity.com/paper/2409.05105/) (2024)
- [Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learning](https://mlsanity.com/paper/2507.04018/) (2025)
- [Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese](https://mlsanity.com/paper/2606.07300/) (2026)

## Abstract (arXiv)

Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC. In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task. We propose SCOPE which builds on top of a shared encoder two parallel decoders, one for the primary CSC task and the other for a fine-grained auxiliary CPP task, with a novel adaptive weighting scheme to balance the two tasks. In addition, we design a delicate iterative correction strategy for further improvements during inference. Empirical evaluation shows that SCOPE achieves new state-of-the-art on three CSC benchmarks, demonstrating the effectiveness and superiority of the auxiliary CPP task. Comprehensive ablation studies further verify the positive effects of adaptivity and granularity of the task. Code and data used in this paper are publicly available at https://github.com/jiahaozhenbang/SCOPE.