# CL²GEC:A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction

**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.

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## 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:

```json
{
  "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.

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## 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):

```bibtex
@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.