Few-Shot Legal Knowledge Question Answering System for COVID-19 Epidemic

Abstract

Recently, the pandemic caused by COVID-19 is severe in the entire world. The prevention and control of crimes associated with COVID-19 are critical for controlling the pandemic. Therefore, to provide efficient and convenient intelligent legal knowledge services during the pandemic, in this paper, we develop an intelligent system for answering legal questions on the WeChat platform. The data source we used for training our system is “The typical cases of national procuratorial authorities handling crimes against the prevention and control of the new coronary pneumonia pandemic following the law”, which is published online by the Supreme People’s Procuratorate of the People’s Republic of China. We base our system on BERT (a well-known pre-trained language model) and use the shared attention mechanism to capture the text information further. Then we train a model to minimise the contrastive loss. Finally, the system uses the trained model to identify the information entered by a user, and accordingly responds to the user with a reference case similar to the query case and give the reference legal gist applicable to the query case.