Multi-task reading for intelligent legal services

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.