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README.md

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+# CL²GEC:A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction
+
+## Dataset Summary
+
+**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:
+
+```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.
+
+---
+
+## 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.