# Disentangled Phonetic Representation for Chinese Spelling Correction

arXiv: https://arxiv.org/abs/2305.14783 · HTML: https://mlsanity.com/paper/2305.14783/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 21 of 21)

- [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)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Self-Distillation Amplifies Regularization in Hilbert Space](https://mlsanity.com/paper/2002.05715/) (2020)
- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [Parallel Iterative Edit Models for Local Sequence Transduction](https://mlsanity.com/paper/1910.02893/) (2019)
- [SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check](https://mlsanity.com/paper/2004.14166/) (2020)
- [The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking](https://mlsanity.com/paper/2203.00991/) (2022)
- [Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding](https://mlsanity.com/paper/2106.04970/) (2021)
- [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://mlsanity.com/paper/2105.12306/) (2021)
- [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://mlsanity.com/paper/2302.06466/) (2023)
- [Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking](https://mlsanity.com/paper/2210.10320/) (2022)
- [Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity](https://mlsanity.com/paper/2210.10996/) (2022)
- [SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking](https://mlsanity.com/paper/2210.17168/) (2022)

## Built on by (most cited first, 14 of 14)

- [C-LLM: Learn to Check Chinese Spelling Errors Character by Character](https://mlsanity.com/paper/2406.16536/) (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)
- [Mixture of Small and Large Models for Chinese Spelling Check](https://mlsanity.com/paper/2506.06887/) (2025)
- [A Training-free LLM-based Approach to General Chinese Character Error Correction](https://mlsanity.com/paper/2502.15266/) (2025)
- [Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning](https://mlsanity.com/paper/2411.17679/) (2024)
- [Chinese Spelling Correction: A Comprehensive Survey of Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2502.11508/) (2025)
- [Bi-DCSpell: A Bi-directional Detector-Corrector Interactive Framework for Chinese Spelling Check](https://mlsanity.com/paper/2406.01879/) (2024)
- [Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction](https://mlsanity.com/paper/2407.15498/) (2024)
- [Contextualized Token Discrimination for Speech Search Query Correction](https://mlsanity.com/paper/2509.04393/) (2025)
- [Automatic Real-word Error Correction in Persian Text](https://mlsanity.com/paper/2407.14795/) (2024)
- [A Coin Has Two Sides: A Novel Detector-Corrector Framework for Chinese Spelling Correction](https://mlsanity.com/paper/2409.04150/) (2024)
- [Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learning](https://mlsanity.com/paper/2507.04018/) (2025)
- [CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards](https://mlsanity.com/paper/2606.00020/) (2026)
- [Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese](https://mlsanity.com/paper/2606.07300/) (2026)

## Abstract (arXiv)

Chinese Spelling Correction (CSC) aims to detect and correct erroneous characters in Chinese texts. Although efforts have been made to introduce phonetic information (Hanyu Pinyin) in this task, they typically merge phonetic representations with character representations, which tends to weaken the representation effect of normal texts. In this work, we propose to disentangle the two types of features to allow for direct interaction between textual and phonetic information. To learn useful phonetic representations, we introduce a pinyin-to-character objective to ask the model to predict the correct characters based solely on phonetic information, where a separation mask is imposed to disable attention from phonetic input to text. To avoid overfitting the phonetics, we further design a self-distillation module to ensure that semantic information plays a major role in the prediction. Extensive experiments on three CSC benchmarks demonstrate the superiority of our method in using phonetic information.