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--- |
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license: cc-by-sa-3.0 |
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language: zh |
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tags: |
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- information-retrieval |
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- question-answering |
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- chinese |
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- wikipedia |
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- open-domain-qa |
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pretty_name: DRCD for Document Retrieval (Simplified Chinese) |
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--- |
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DRCD for Document Retrieval (Simplified Chinese) |
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This dataset is a reformatted version of the [Delta Reading Comprehension Dataset (DRCD)](https://github.com/DRCKnowledgeTeam/DRCD), converted to Simplified Chinese and adapted for **document-level retrieval** tasks. |
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## Summary |
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The dataset transforms the original DRCD QA data into a **document retrieval** setting, where queries are used to retrieve **entire Wikipedia articles** rather than individual passages. Each document is the full text of a Wikipedia entry. |
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The format is compatible with the data structure used in the **[LongEmbed benchmark]((https://github.com/THU-KEG/LongEmbed))** and can be directly plugged into LongEmbed evaluation or training pipelines. |
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## Key Features |
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- 🔤 **Language**: Simplified Chinese (converted from Traditional Chinese) |
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- 📚 **Domain**: General domain, from Wikipedia |
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- 📄 **Granularity**: **Full-document retrieval**, not passage-level |
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- 🔍 **Use Cases**: Long-document retrieval, reranking, open-domain QA pre-retrieval |
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## File Structure |
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### `corpus.jsonl` |
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Each line is a single Wikipedia article in Simplified Chinese. |
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```json |
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{"id": "doc_00001", "title": "心理", "text": "心理学是一门研究人类和动物的心理现象、意识和行为的科学。..."} |
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``` |
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### `queries.jsonl` |
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Each line is a user query (from the DRCD question field). |
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``` json |
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{"qid": "6513-4-1", "text": "威廉·冯特为何被誉为“实验心理学之父”?"} |
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``` |
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### `qrels.jsonl` |
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Standard relevance judgments mapping queries to relevant documents. |
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``` json |
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{"qid": "6513-4-1", "doc_id": "6513"} |
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``` |
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This structure matches [LongEmbed benchmark](https://github.com/dwzhu-pku/LongEmbed)'s data format, making it suitable for evaluating long-document retrievers out of the box. |
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## Example: Document Retrieval Using BM25 |
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You can quickly try out document-level retrieval using BM25 with the following code snippet: |
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https://gist.github.com/ihainan/a1cf382c6042b90c8e55fe415f1b29e8 |
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Usage: |
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``` |
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$ python test_long_embed_bm25.py /home/ihainan/projects/Large/AI/DRCD-Simplified-Chinese/ir_dataset/train |
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...0 |
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Building prefix dict from the default dictionary ... |
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Loading model from cache /tmp/jieba.cache |
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Loading model cost 0.404 seconds. |
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Prefix dict has been built successfully. |
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...200 |
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...400 |
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...600 |
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...800 |
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...1000 |
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...1200 |
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...1400 |
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...1600 |
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...1800 |
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Acc@1: 64.76% |
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nDCG@10: 76.61% |
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``` |
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## License |
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The dataset is distributed under the Creative Commons Attribution-ShareAlike 3.0 License (CC BY-SA 3.0). You must give appropriate credit and share any derivative works under the same terms. |
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## Citation |
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If you use this dataset, please also consider citing the original DRCD paper: |
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```graphql |
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@inproceedings{shao2018drcd, |
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title={DRCD: a Chinese machine reading comprehension dataset}, |
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author={Shao, Chih-Chieh and Chang, Chia-Hsuan and others}, |
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booktitle={Proceedings of the Workshop on Machine Reading for Question Answering}, |
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year={2018} |
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} |
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``` |
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## Acknowledgments |
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- Original data provided by Delta Research Center. |
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- This project performed format adaptation and Simplified Chinese conversion for IR use cases. |
