# Overview of CTC 2021: Chinese Text Correction for Native Speakers

arXiv: https://arxiv.org/abs/2208.05681 · HTML: https://mlsanity.com/paper/2208.05681/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 11 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (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, 6 of 6)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [On the (In)Effectiveness of Large Language Models for Chinese Text Correction](https://mlsanity.com/paper/2307.09007/) (2023)
- [CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers](https://mlsanity.com/paper/2211.08788/) (2022)
- [FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotation](https://mlsanity.com/paper/2311.04906/) (2023)
- [A Training-free LLM-based Approach to General Chinese Character Error Correction](https://mlsanity.com/paper/2502.15266/) (2025)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)

## Abstract (arXiv)

In this paper, we present an overview of the CTC 2021, a Chinese text correction task for native speakers. We give detailed descriptions of the task definition and the data for training as well as evaluation. We also summarize the approaches investigated by the participants of this task. We hope the data sets collected and annotated for this task can facilitate and expedite future development in this research area. Therefore, the pseudo training data, gold standards validation data, and entire leaderboard is publicly available online at https://destwang.github.io/CTC2021-explorer/.