mlsanityData to 14 Oct 2026

CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

arXiv 1912.09156 ↗First posted 19 Dec 2019cs.CL cs.LGcited by 26

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Abstract (arXiv)

We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers. The documents come from judgment documents and the questions are annotated by law experts. The CJRC dataset can help researchers extract elements by reading comprehension technology. Element extraction is an important task in the legal field. However, it is difficult to predefine the element types completely due to the diversity of document types and causes of action. By contrast, machine reading comprehension technology can quickly extract elements by answering various questions from the long document. We build two strong baseline models based on BERT and BiDAF. The experimental results show that there is enough space for improvement compared to human annotators.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Dec 2019.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 8; 2024: 4; 2025: 5; 2026: 2.

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  8. 2025VLQA: The First Comprehensive, Large, and High-Quality Vietnamese Dataset for Legal Question Answeringcited by 4
  9. 2026CoAL-RAG: A Complexity-Aware Legal Retrieval-Augmented Generation Methodcited by 3
  10. 2023Analyzing Hong Kong's Legal Judgments from a Computational Linguistics point-of-viewcited by 1
  11. 2024LFED: A Literary Fiction Evaluation Dataset for Large Language Modelscited by 1
  12. 2025Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Modelscited by 1
  13. 2025CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in Chinacited by 1
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