Since legal data contains both structured data and unstructured data, it is a great challenge to implement machine reading comprehension technology in empirical analysis of law. This paper proposes a multi-tasking reading for intelligent legal services, which applies statistical analysis and machine reading comprehension techniques, and can process both structured and unstructured data. At the same time, this paper proposes a machine reading comprehension model that can perform multi-task learning, LegalSelfReader, which can solve the problem of diversity of questions. In the experiment of the legal reading comprehension dataset CJRC, the model proposed in this paper is far superior to the two classic models of BIDAF and Bert in three evaluation indicators. And our model is also better than some models published by HFL(Harbin Institute of Technology and iFly Joint Lab), and has also achieved lower consumption in training costs. At the same time, in the experiment of visualizing the attention value, it also demonstrates that the model proposed in this paper has a stronger ability to extract evidence.

由于法律数据既包含结构化数据又包含非结构化数据，因此在法律实证分析中实现机器阅读理解技术是一个巨大的挑战。本文提出了一种用于智能法律服务的多任务阅读，该阅读应用了统计分析和机器阅读理解技术，可以处理结构化和非结构化数据。同时，本文提出了一种可以执行多任务学习的机器阅读理解模型LegalSelfReader，可以解决问题的多样性。在法律阅读理解数据集CJRC的实验中，本文提出的模型在三个评估指标上远远优于BIDAF和Bert的两个经典模型。而且我们的模型也比HFL（哈尔滨工业大学和iFly联合实验室）发布的某些模型更好，并且还降低了培训成本。同时，在视觉化注意力价值的实验中，也表明本文提出的模型具有较强的证据提取能力。