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.

本研究介绍了一个含有近 10K 文档和近 50K 个问题及其答案的中国司法阅读理解 (CJRC) 数据集，基于 Bert 和 BiDAF 构建了两个强基准模型，实验结果显示与人工标注员的表现相比仍有进步的空间。该数据集可用于司法领域中元素提取的机器阅读理解技术研究。