| --- |
| license: cc-by-sa-3.0 |
| language: zh |
| tags: |
| - information-retrieval |
| - question-answering |
| - chinese |
| - wikipedia |
| - open-domain-qa |
| pretty_name: DRCD for Document Retrieval (Simplified Chinese) |
| --- |
| |
| DRCD for Document Retrieval (Simplified Chinese) |
|
|
| 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. |
|
|
| ## Summary |
|
|
| 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. |
|
|
| 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. |
|
|
| ## Key Features |
|
|
| - 🔤 **Language**: Simplified Chinese (converted from Traditional Chinese) |
| - 📚 **Domain**: General domain, from Wikipedia |
| - 📄 **Granularity**: **Full-document retrieval**, not passage-level |
| - 🔍 **Use Cases**: Long-document retrieval, reranking, open-domain QA pre-retrieval |
|
|
| ## File Structure |
|
|
| ### `corpus.jsonl` |
|
|
| Each line is a single Wikipedia article in Simplified Chinese. |
|
|
| ```json |
| {"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。..."} |
| ``` |
|
|
| ### `queries.jsonl` |
|
|
| Each line is a user query (from the DRCD question field). |
|
|
| ``` json |
| {"qid": "6513-4-1", "text": "威廉·冯特为何被誉为“实验心理学之父”?"} |
| ``` |
|
|
| ### `qrels.jsonl` |
|
|
| Standard relevance judgments mapping queries to relevant documents. |
|
|
| ``` json |
| {"qid": "6513-4-1", "doc_id": "6513"} |
| ``` |
|
|
| 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. |
|
|
| ## Example: Document Retrieval Using BM25 |
|
|
| You can quickly try out document-level retrieval using BM25 with the following code snippet: |
|
|
| https://gist.github.com/ihainan/a1cf382c6042b90c8e55fe415f1b29e8 |
|
|
| Usage: |
|
|
| ``` |
| $ python test_long_embed_bm25.py /home/ihainan/projects/Large/AI/DRCD-Simplified-Chinese/ir_dataset/train |
| ...0 |
| Building prefix dict from the default dictionary ... |
| Loading model from cache /tmp/jieba.cache |
| Loading model cost 0.404 seconds. |
| Prefix dict has been built successfully. |
| ...200 |
| ...400 |
| ...600 |
| ...800 |
| ...1000 |
| ...1200 |
| ...1400 |
| ...1600 |
| ...1800 |
| |
| Acc@1: 64.76% |
| nDCG@10: 76.61% |
| ``` |
|
|
| ## License |
| 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. |
|
|
| ## Citation |
| If you use this dataset, please also consider citing the original DRCD paper: |
|
|
| ```graphql |
| @inproceedings{shao2018drcd, |
| title={DRCD: a Chinese machine reading comprehension dataset}, |
| author={Shao, Chih-Chieh and Chang, Chia-Hsuan and others}, |
| booktitle={Proceedings of the Workshop on Machine Reading for Question Answering}, |
| year={2018} |
| } |
| ``` |
|
|
| ## Acknowledgments |
| - Original data provided by Delta Research Center. |
| - This project performed format adaptation and Simplified Chinese conversion for IR use cases. |
