# CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers

arXiv: https://arxiv.org/abs/2211.08788 · HTML: https://mlsanity.com/paper/2211.08788/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [LCSTS: A Large Scale Chinese Short Text Summarization Dataset](https://mlsanity.com/paper/1506.05865/) (2015)
- [FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.12364/) (2022)
- [Chinese Lexical Analysis with Deep Bi-GRU-CRF Network](https://mlsanity.com/paper/1807.01882/) (2018)
- [Exploring and Adapting Chinese GPT to Pinyin Input Method](https://mlsanity.com/paper/2203.00249/) (2022)
- [Overview of CTC 2021: Chinese Text Correction for Native Speakers](https://mlsanity.com/paper/2208.05681/) (2022)

## Built on by (most cited first, 13 of 13)

- [CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards](https://mlsanity.com/paper/2512.23971/) (2025)
- [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)
- [OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2402.13524/) (2024)
- [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)
- [SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant?](https://mlsanity.com/paper/2503.06029/) (2025)
- [ChineseErrorCorrector3-4B: State-of-the-Art Chinese Spelling and Grammar Corrector](https://mlsanity.com/paper/2511.17562/) (2025)
- [Vision Language Models Are Not (Yet) Spelling Correctors](https://mlsanity.com/paper/2509.17418/) (2025)
- [Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction](https://mlsanity.com/paper/2311.08219/) (2023)
- [GUIDE: Generative Unsupervised Chinese Query Correction via Phonetic and Visual Shared-ID Encoding](https://mlsanity.com/paper/2608.25343/) (2026)
- [ReViCo: Unveiling the Limitations of VLMs in Visual Text Understanding via Error Correction](https://mlsanity.com/paper/2608.27154/) (2026)

## Abstract (arXiv)

In this paper, we present CSCD-NS, the first Chinese spelling check (CSC) dataset designed for native speakers, containing 40,000 samples from a Chinese social platform. Compared with existing CSC datasets aimed at Chinese learners, CSCD-NS is ten times larger in scale and exhibits a distinct error distribution, with a significantly higher proportion of word-level errors. To further enhance the data resource, we propose a novel method that simulates the input process through an input method, generating large-scale and high-quality pseudo data that closely resembles the actual error distribution and outperforms existing methods. Moreover, we investigate the performance of various models in this scenario, including large language models (LLMs), such as ChatGPT. The result indicates that generative models underperform BERT-like classification models due to strict length and pronunciation constraints. The high prevalence of word-level errors also makes CSC for native speakers challenging enough, leaving substantial room for improvement.