Datasets:

QShane
/

CL2GEC

CL²GEC is a benchmark for Chinese grammatical error correction (GEC) in scholarly writing with a continual-learning protocol. The corpus covers 10 first-level disciplines (Law, Management, Education, Economics, Natural Sciences, History, Agricultural Sciences, Literature, Arts, Philosophy). Each sample contains an errorful sentence (source) and one or more corrected references (references). Standard train / validation / test splits are provided and may be used per-discipline to study sequential/continual learning behavior such as forgetting and transfer.

Supported Tasks and Leaderboards

Grammatical Error Correction (GEC) / Text-to-Text Generation

Input: a Chinese sentence containing grammatical/usage errors.
Output: a semantically equivalent, grammatically correct sentence.

Recommended Metrics

GEC metrics: Precision / Recall / F0.5 (e.g., via ChERRANT).
Continual-learning (optional): Average Performance and Backward Transfer (BWT) computed over task sequences defined by the ordered disciplines.

Dataset Structure

Data Instances

Below is a recommended public JSON schema:

{
  "id": "0",
  "source": "总体上看，仍有许多案件以不适用调解制度。",
  "references": [
    "总体上看，依然有许多案件不适宜使用调解制度来解决。"
  ],
  "category": "法学",
  "edits": [
    {
      "src_interval": [7, 9],
      "tgt_interval": [7, 9],
      "src_content": ["不", "适", "用"],
      "tgt_content": ["不", "适", "宜"]
    }
  ]
}

Data Fields

id (string): unique sample identifier.
source (string): original sentence with errors.
references (list[string]): one or more corrected sentences.
category (string): first-level discipline.
edits (list[object], optional): token/character-level edits (if provided).

Data Splits

Split	#Samples	Notes
train	7,000	training data
validation	1,000	development set
test	2,000	held-out evaluation

Categories (Disciplines)

Below are the 10 discipline labels (Chinese) with suggested English names:

Chinese (label in data)	English
法学	Law
管理	Management
教育	Education
经济学	Economics
理学	Sciences
历史学	History
农学	Agronomy
文学	Literature
哲学	Philosophy
艺术学	Arts

Collection and Annotation

Sources: Extracted from CNKI Academic PDFs, covering 10 first-level disciplines and 100 second-level disciplines; only abstracts and main text are retained; non-linguistic content such as references, acknowledgments, formulas, tables, and figure captions are removed; sentence-level segmentation uses LTP. Anonymization is also performed.
Annotation:
1. Multi-model consistency error detection to screen candidates (e.g., GECToR, Chinese-BART, etc.);
2. LLM pre-rewrite as weak references;
3. Dual independent annotation (by senior annotators with the same subject background), unifying style, revision, and merging;
4. 100% review by domain experts to ensure publication-level quality, supplementing with multiple references when necessary.

Intended Uses

Research on Chinese GEC for scholarly prose.
Cross-domain robustness and discipline-aware modeling.
Continual learning studies focusing on forgetting/transfer across disciplines.

Ethical Considerations & Privacy

Texts are anonymized and cleaned to remove sensitive information.
Sentences are taken from academic texts and contain academic terminology; when the model is made available for public use, the risks and scope of application should be declared and misuse should be avoided.
Ensure that upstream content complies with platform/journal usage policies and your chosen license clearly states permitted uses.

Citation

If you use this dataset in your research, please cite (replace with your paper details):

@misc{qin2025cl2gec,
  title   = {CL$^2$GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction},
  author  = {Shang Qin and Jingheng Ye and Yinghui Li and Hai-Tao Zheng and Qi Li and Jinxiao Shan and Zhixing Li and Hong-Gee Kim},
  year    = {2025},
  eprint  = {2509.13672},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url     = {https://arxiv.org/abs/2509.13672}
}

Changelog

v1.0.0: initial public release; includes train/validation/test splits, field schema, usage examples, and evaluation guidance.